HOW WELL IS THE RUSSIAN WHEAT MARKET FUNCTIONING?

A COMPARISON WITH THE CORN MARKET IN THE USA

Miranda Svanidze, Linde Götz

Leibniz Institute of Agricultural Development in Transition Economies

(IAMO), Halle (Saale), Germany

2017

Copyright 2017 by authors. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

Paper prepared for presentation at the 57th annual conference of the

GEWISOLA (German Association of Agricultural Economists)

and the 27th annual conference of the ÖGA (Austrian Society of Economics)

“Bridging the Gap between Resource Efficiency and Society’s Expectations in

the Agricultural and Food Economy”

Munich, Germany, September 13 – 15, 2017

Svanidze@iamo.de

HOW WELL IS THE RUSSIAN WHEAT MARKET FUNCTIONING?

A COMPARISON WITH THE CORN MARKET IN THE USA

Abstract

Given Russia’s leading position in the world wheat trade, how well its grain markets function

becomes very important question to evaluate the state of future global food security. We use a

threshold vector error correction model to explicitly account for the influence of trade costs and

distance on price relationships in the grain markets of Russia and the USA. In addition, we study

impact of market characteristics on regional wheat market integration. Empirical evaluation

shows that distance between markets, interregional trade flows, export orientation, export tax and

export ban all have a significant impact on the magnitude of wheat market integration.

Keywords

regional market integration, threshold vector error correction model, grain markets, Russia, USA,

export ban.

1 Introduction

In recent years Russia has advanced from a grain importing country to one of the primary grain

exporting countries. In 2016/17 Russia is forecasted to become the largest wheat exporter in the

world (Interfax 2016).

Russia could further boost its grain production by increasing production efficiency and also by re-

cultivating formerly abandoned agricultural land. According to OECD/FAO (2012) global grain

production needs to increase by 30% to satisfy global demand for cereals which will reach 3

billion tons by 2050. Russia could play a large role for future global food security (Lioubimtseva

and Henebry, 2012). This requires not only Russia’s large additional grain production potential to

be mobilized but also that grain markets are functioning well enabling that the grain exporting

potential is mobilized as well.

This study aims to address the research question how well the Russian grain market is

functioning, a question which has not been addressed in the literature before. Following a price

transmission approach we are focusing on the primary grain producing regions and investigate the

integration of the regional grain markets. To what degree and how fast are price shocks in one

region transmitted to the other regions?

This is an important question given that the Russian grain market is characterized by strong

production volatility resulting from extreme weather events which are expected to increase with

climate change. Favourable production conditions and thus relatively high yields can be observed

in some regions but relatively low yields in other regions at the same time. Therefore,

interregional grain trade is of high importance to equilibrate grain supply and demand within

Russia. Nonetheless, grain market transport and storage infrastructure is deficient in several

regions and price peaks are repeatedly observed on regional markets, exceeding even the world

market price.

In a well-functioning, efficient market, with a well-developed transport and storage infrastructure,

regional prices differ at most by the costs of trade between those regions. Also, price shocks in

one region are quickly transmitted to the other regions inducing interregional trade flows when

price differences exceed trade costs (Fackler and Goodwin, 2001). Thus, an efficient market could

also contribute to cushioning price increasing effects of regional harvest shortfalls and prevent

that prices increase beyond the world market price.

However, Russia has a history of restricting the exports of wheat to the world market when

domestic wheat prices peak. As our second question, we investigate the effects of the wheat

2

export ban 2010/11 on regional price relationships to shed further light on the domestic price

effects of export controls. This is an addition to Götz et al. 2013, 2016 which focus on the export

controls’ effects on the integration in the world market.

We address both research questions in a price transmission framework. We apply a threshold

vector error-correction model (TVECM) to explicitly account for the influence of distance and use

a Bayesian estimator suggested by Greb et al. (2013) as an alternative to the conventional

maximum likelihood approach (Hansen and Seo, 2002; Lo and Zivot, 2001).

Highly integrated markets characterized by strong price relationships with fast transmission of

price changes between the regions are usually interpreted as evidence for well-functioning

markets. However, the Russian wheat market is characterized by extremely large distances of up

to 4000 km which certainly negatively affects market integration.

To assess how well the Russian market is functioning we conduct a comparative price

transmission analysis for the corn market of the USA which is also characterized by large

distances, strong variation in regional production and high interregional trade flows. We assume

that the corn market of the USA is one of the most efficient grain markets in the world

characterized by well-developed transport and storage infrastructure and high market

transparency, serving as a benchmark for the Russian wheat market in this study.

The remainder of this paper is organized as follows. In the next section, we discuss market

conditions and the consequences of the export ban 2010/11 for wheat trade in Russia. This is

followed by the review of major literature sources. The detailed presentation of econometric

model is given in the section on methodology and estimation. Data section 5 focuses on the

properties and preliminary assessment of time series used in analysis. In the results section6, we

discuss outcomes of model estimation. The results for the Russian wheat market are compared to

the results for the corn market of the USA presented in section 7. In the final section 8,

concluding remarks are given.

2 Characteristics of the Russian wheat markets

2.1 Regional wheat production and harvest shortfall

Wheat production in Russia is concentrated on a limited, yet spatially protracted area. Two large

regional production clusters emerge depending on both the type of culture and the area of

cultivation. The winter wheat cluster covers Southwest of Russia stretching from the Black Sea to

Volga. Yields in this area amount to 3 tons per hectare on average (2006-2010). The spring wheat

cluster spans over Urals and West Siberia. In contrast to winter wheat, spring wheat is much less

productive with yields amounting to 1.7 tons per hectare on average (2006-2010).

During the last decade, 2010 marks as the year when Russian grain markets witnessed

exeptionally low production. Drought in 2010 affected the key crop growing areas in Russia

(Figure 1), whcih resulted in the unusually low harvest and export-respricting policies by the

government.

Figure 1. Map of crop-growing regions affected by drought in 2010

3

Source: Own illustration.

In general, the size of wheat production in the major grain production regions differs strongly. As

can be seen from Figure 2, wheat production is highest in North Caucasus, with an annual

production varying between 12 and 22 million tons in the period 2005-2013. This is followed by

Volga and West Siberia (with wheat production varying between 4 and 11 million tons in each

region), Black Earth (between 3 to 9 million tons), Urals (between 2 to7 million tons) and Central

(2-4 million).

Figure 2. Regional wheat production development in Russia

Source: Götz et al. 2016

In addition, the variation of wheat production within a region is also extremely high. Table 1

gives regional wheat production as the average of wheat production in the previous three years. It

becomes evident that for example in the Volga region, wheat production varied between 34% and

143% in the marketing years 2005/6 to 2012/13. Weather conditions are a key determinant of the

quantity of wheat production. Due to large distances, the production regions are affected by

different climatic and weather conditions.

Table 1: Regional wheat production developments Russia (2005-2013), in % of the average

of previous 3 years

20000

28000

36000

44000

52000

60000

0

5000

10000

15000

20000

25000

2005 2006 2007 2008 2009 2010 2011 2012 2013

10

00

t

10

00

t

North Caucasus West Siberia Volga Black Earth

Ural Central Total

4

2005/6 2006/07 2007/08 2008/9 2009/10 2010/11 2011/12 2012/13

North Caucasus 126 112 98 139 107 104 108 68

Central 109 100 117 160 159 81 79 97

Black Earth 120 97 117 187 136 46 86 96

Volga 112 103 98 143 109 34 81 84

West Siberia 93 98 114 99 140 86 96 46

Ural 91 118 125 117 87 38 144 61 Source: Götz et al. 2016

This implies that favorable production conditions and thus relatively high yields might be

observed in some regions but relatively low yields in other regions at the same time. For example,

grain production was even by 4% above average in North Caucasus in the marketing year

2010/11, whereas the regions Volga, Urals and Black Earth were severely hit by the drought with

grain production 66%, 62% and 54% below average, respectively.

2.2 Regional wheat trade and transportation costs

The Russian wheat producing regions can be classified into surplus and deficit areas. The former

includes North Caucasus, Black Earth, Volga, West Siberia and Urals, which usually supply their

production excess to other markets. Central region with Moscow is the primary wheat deficit

region, which heavily depends on external supplies. Central mostly imports wheat domestically

from the regions Black Earth, Volga, Urals and West Siberia. By contrast, North Caucasus

supplies primarily to the world markets, while its role in the domestic trade is rather limited. Due

to the presence of high-capacity sea terminals, North Caucasus also serves as a gate-market for

the other grain producing regions, particularly Volga and Black Earth, to export wheat to the

world market. Differing, Urals and West Siberia are far away from not only the world market,

with the distance to the Black Sea ports amounting up to 4000 km, but also the grain consumption

regions within Russia. In particular, Moscow is about 2000-3000 km apart. Due to outdated and

insufficient transport infrastructure, Urals and West Siberia are not well connected neither world

market nor the consumption centers.

However, due to the large variation in grain production, the size of trade flows between surplus

and deficit regions may vary strongly.

Trains and trucks are the two primary means of wheat transportation in Russia. Trains are mostly

used when the transport distance between regions exceeds 1000 kilometers, while trucks are often

preferred on shorter routes.

As production areas cover large territory, the influence of transport infrastructure is crucial for the

distribution of wheat. The quality of transport infrastructure strongly differs between regions. For

instance, the density of the railway network is highest in the European part of Russia, whereas it is

much lower in Urals and West Siberia. It is reported that excessive crops are often difficult to

transport beyond West Siberia as the only railway track connecting the area to the rest of the

country has low throughput capacity and is shared by many other industries (Scherbanin, 2012).

In addition, grain traders regularly complain that the number of grain wagons in peak seasons

does not suffice (Gonenko, 2011).

The estimated values of the railway delivery fees for selected market pairs in 2010 are presented

in Table 2. It should be pointed out that the delivery fee captures only parts of the full transport

costs. Other expenses may include storage fees, transportation between the railway station and the

grain processing facility, insurance premium etc. The share of the delivery fee in the total

transport costs varies significantly amounting to 30% to 70% of transport costs.

Table 2. Costs of wheat transportation between the selected locations in 2010

Pair of

markets

Station of

origin

Station of

destination

Distance

(km)

Delivery fee

(RUB/ton)

Delivery fee

(USD/ton)

North Caucasus-Black Earth Kavkaz Voronezj 870 781 26

5

North Caucasus-Central Kavkaz Moscow 1300 1165 39

North Caucasus-Volga Kavkaz Kazan' 1708 1328 45

Volga-Central Kazan' Moscow 812 752 25

Urals-Central Kurgan Moscow 2037 1498 50

West Siberia-North Caucasus Novosibirsk Kavkaz 3800 2576 86

West Siberia-Central Novosibirsk Moscow 3350 2147 72

Note: Delivery fee is recognized as a charge due to be paid for the rent of one wagon (measured in RUB/ton). The

value of delivery fee is estimated using an online calculator provided by the Russian railways on August 06, 2010

when trade was freely possible. These estimates correspond to the amount of wheat which is simultaneously

transferred in a group of 100 wagons. Therefore, they may slightly vary if the actual number of wagons included in

the group differs.

Source: Own illustration, data: Rosstat 2015.

3 Literature review

This paper adds to the strand of literature focusing on spatial price relations between regional

agricultural markets.

Goodwin and Piggott (2001) first introduced threshold co-integration in the spatial price

transmission literature. They analyse spatial price links between regional corn and soybean

markets in North Carolina using a two-regime threshold autoregressive (TAR) model. They find

that thresholds are proportionally related to transaction costs, which increase with distance

between the markets. Their study confirms the presence of non-linear adjustment of prices to

deviations from the long-run price equilibrium between two locations. In particular, price

adjustment is hardly confirmed if regional price differences are smaller than transaction costs. On

the contrary, large price differentials induce adjustment of regional prices to their price

equilibrium, which increases with proximity of the markets. Additionally, the authors utilize a

three-regime threshold vector error-correction model (TVECM) to account for changes in the

direction of trade flows. However, model results do not find evidence that a reversal in trade

direction alters the speed of price adjustments to its spatial price equilibrium.

Several methods have been developed to correctly identify the optimal threshold parameter. Chan

(1993) offers the method of threshold selection that gained recognition in the context of the TAR

model. According to this approach, the optimal threshold is to be chosen from the set of residuals

retrieved from the long-run equilibrium regression. The residuals are sorted using results of sum

of squared errors (SSE), and the residual with the lowest SSE is selected as a threshold. Hansen

and Seo (2002) use values of error-correction terms (ECTs) to determine possible threshold

adjustment in a two-regime TVECM. They pair ECTs with corresponding values of the co-

integrating vector to construct a two-dimensional grid and then estimate this grid with maximum

likelihood. The pair that yields the lowest value of the concentrated likelihood function is

determined to contain the optimal threshold parameter. These procedures are criticized for the

reliance on an arbitrarily chosen trimming parameter which is used to ensure that the model

parameters of each regime are estimated based on a minimum number of observations. According

to Greb et al. (2014) the selection of the trimming parameter might lead to the exclusion of the

true value of a threshold from the threshold parameter space and, as a consequence, to unreliable

threshold values and model parameter estimates.

Balcombe et al. (2007) offer an alternative framework to estimate the parameters of generalized

threshold error-correction model on the basis of classic Bayesian theory. They apply this model to

monthly wheat, maize and soya prices for the United States, Argentina and Brazil. Results suggest

that the new method is capable of addressing the problem of identification of model parameters

that often pertains to the maximum likelihood approach. This problem results from the jagged

nature of the maximum likelihood function implying that the function cannot be evaluated using

traditional differentiation methods. By contrast, classic Bayesian analysis offers special

computational algorithms which allow estimating the parameters without using the irregular

maximum likelihood function.

6

Greb et al. (2014) use a methodologically similar empirical Bayesian paradigm to develop a

threshold estimator in the context of generalized threshold models. However, in comparison to the

Bayesian approach followed in Balcombe et al. (2007), they tend to reduce the application of so-

called non-informative priors. According to Greb et al. (2014), certain prior values should be

assigned to the model parameters to make the estimation procedure possible, but in the absence of

any preliminary information this assignment becomes rather arbitrary and may influence the final

estimates. To avoid this outcome, they start with selected priors obtained from maximum

likelihood estimation. Additionally, the empirical Bayesian analysis requires no trimming

parameter to achieve the desired distribution of observations across regimes. Greb et al. (2013)

exploit this approach and compare it to the maximum likelihood procedure to revisit the study of

Goodwin and Piggott (2001). Applying three-regime TVECM, they conclude that the Bayesian

estimator identifies larger thresholds and wider inaction bands compared to the maximum

likelihood counterpart. Moreover, they also find more evidence in support of asymmetric

adjustment that takes place, potentially, due to changes in the direction of trade.

Our study also contributes to the growing price transmission literature on the domestic price

effects of export controls. The effects of wheat export controls in Russia were previously

addressed within a price transmission approach by Götz et al. (2016) and Götz et al. (2013). Both

studies focus on the relationship between the world market price and the domestic prices in order

to identify the price dampening effect of the export controls. Götz et al. (2013) investigate

domestic price effects of the export tax in Russia during 2007/8 within a MSECM approach. They

find compared to Ukraine a rather low price dampening effect amounting to 25%. Results of Götz

et al. (2016) suggest a strong heterogeneity of the price dampening effect of the wheat export ban

2010/11 in Russia, varying between 67% and 35% in the major grain producing regions.

Differing, this study investigates how the export ban 2010/11 impacts price relationships between

the grain producing regions of Russia themselves. A further novelty of our approach is that we use

a TVECM in order to capture the possible effects of the export ban on trade costs. Also, we are

supplementing the regional price data with interregional trade flow data to facilitate interpretation

of our model results.

A regional perspective is also followed by Baylis et al. (2014) which investigate the export ban

for wheat and rice implemented in India 2007-2011. They take into account integration between

the world and domestic markets, but also explicitly focus on price relations between the regions of

India. The analysis is based on regional price data for producing, consuming and port markets and

the world market price. Using a linear VECM and a TVECM, they investigate cointegration and

integration for the time period when trade was freely possible and compare it to when the export

ban was implemented. They find for rice all port markets integrated with the world market during

the export ban period as well as when trade is freely possible. Though, no cointegration of the port

markets and the world market for wheat is observed during the export ban. However, more

domestic market price pairs are integrated during the export ban for rice but less for wheat, when

compared to the free trade regime.

4 Methodological framework and data properties

4.1 Measurement of market integration

Regionally integrated markets are related through a long-run equilibrium parity, which we

characterize by long-run price transmission elasticities estimated in the cointegration equation.

Price transmission elasticities characterize how strongly are price shocks transmitted from one

region to another. Long-run price equilibrium is given as:

𝑃𝑡1 = α + β𝑃𝑡

2 + 𝜀𝑡 (1)

where 𝑃𝑡1 and 𝑃𝑡

2 are natural logarithm of prices at time t for every regional market pair, ε𝑡 denotes stationary disturbance term. α denotes intercept and β is coefficient of the long-run price

7

transmission elasticity, characterizing the magnitude of the transmission of price shocks from one

market to another. Regression equation is estimated by the ordinary least squares method.

Usually, market prices are tend to diverge from the long-run equilibrium parity from time to time.

Threshold vector error correction model (TVECM) is designed to examine how fast prices

converge back to the equilibrium in the short-run. We adopt a non-linear 3-regime TVECM with

2 thresholds developed by Greb et al. (2013) also to account for the influence of trade costs,

which are highly relevant to the Russian wheat market.

A three-regime TVECM is illustrated in equation (2). The vector of dependent variables ∆𝑃𝑡 =(∆𝑃𝑡1, ∆𝑃𝑡

2) denotes the difference between prices in periods 𝑡 and 𝑡 − 1 for both markets in

question. As the independent variables, 𝜀𝑡−1, error correction term, or alternatively, lagged

residuals from equation (1) is taken to represent the price deviation from the long-run price

equilibrium. Additionally, ∑ ∆𝑃𝑡−𝑚𝑀𝑚=1 term is the sum of price differences lagged by period m to

correct residual correlation, and 𝜔𝑡 denotes a white-noise process with expected value 𝐸(𝜔𝑡) = 0 and covariance matrix 𝐶𝑜𝑣(𝜔𝑡) = Ω ∈ (ℝ

+)2×2.

∆𝑃𝑡 =

�

𝜌1𝜀𝑡−1 + Θ1𝑚∆𝑃𝑡−𝑚 + 𝜔𝑡

𝑀

𝑚=1, 𝑖𝑓 𝜀𝑡−1 ≤ 𝜏1 (𝐿𝑜𝑤𝑒𝑟)

𝜌2𝜀𝑡−1 + Θ2𝑚∆𝑃𝑡−𝑚 + 𝜔𝑡𝑀

𝑚=1, 𝑖𝑓 𝜏1 < 𝜀𝑡−1 ≤ 𝜏2 (𝑀𝑖𝑑𝑑𝑙𝑒 )

𝜌3𝜀𝑡−1 + Θ3𝑚∆𝑃𝑡−𝑚 + 𝜔𝑡𝑀

𝑚=1, 𝑖𝑓 𝜏2 < 𝜀𝑡−1 (𝑈𝑝𝑝𝑒𝑟)

(2)

The short-run dynamics are characterized by the speed of adjustment parameter (𝜌𝑘) and the

coefficients of the price differences (Θ𝑘𝑚) lagged by m-periods with k referring to a regime. All

parameters may vary by regime with k=1 … 3.

Price observations are attributed to a certain regime depending on the size of the ECT. The 3-

regime TVECM is based on the assumption that two thresholds exist corresponding to the costs of

trade in both directions, i.e. from one market to the other and vice versa. Price observations for

which the ECT is smaller than threshold 𝜏1 are attributed to the lower regime, whereas price

observations with an ECT larger than threshold 𝜏2 are assigned to the upper regime 3. The

threshold is considered a proxy for transaction costs of wheat trade between the two respective

markets. If the ECT is of the size smaller than threshold 𝜏2 but larger than threshold 𝜏1, the

observations are allocated to the middle regime. Within this regime, the difference between the

prices of two regions are smaller than transaction costs of trade.

The speed of adjustment refers to the time period required by the price of a certain market to

correct a deviation from the long-run equilibrium between the two markets. The speed of

adjustment may differ between the regimes. Prices in two spatially separated markets may be

related by trade arbitrage only if the price differences are at least as high as trade costs. This is

given for price observations which are attributed to the upper and lower regime in a 3-regime

TVECM. However, prices may be related but at a lower degree even if the price differences are

smaller than transaction costs, corresponding to the middle regime in a 3-regime TVECM, via

information flows or third markets (Stephens et al., 2012).

There are several conditions that should be satisfied to ensure the stability of the system in (1).

First of all, the speed of adjustment parameters in one specific regime should be of opposite sign

reflecting that markets return to their equilibrium path in the long-run. From (1) it follows that

both markets can be treated as dependent simultaneously such that in each regime ∆𝑃1,𝑡 =𝜌𝑘1𝛾

′𝑃𝑡−1 and ∆𝑃2,𝑡 = 𝜌𝑘2𝛾′𝑃𝑡−1. Convergence is achieved if 𝜌𝑘1 ≤ 0 and 𝜌𝑘2 ≥ 0. Given this

restriction, it is considered sufficient that at least one adjustment parameter in a specific regime is

found significant. Secondly, the difference between the two speed of adjustment prameters of the

outer regimes should fall in the following interval 0 < 𝜌𝑘2 − 𝜌𝑘1 < 1. The last restriction

corresponds to price fluctuations decaying gradually (Greb et al., 2013).

8

We employ novel regularized Bayesian technique to identify estimates of threshold parameters,

which govern the regime switch, and restricted maximum likelihood method to estimate model

variable coefficients (Greb et al., 2013).

The presented model is estimated by two methods and within three steps. First, the long-run price

equilibrium in equation (1) is estimated by ordinary least squares (OLS) method. We retrieve the

error term which enters the TVECM lagged by one period as the ECT variable. Second, the

threshold parameters in equation (2) are identified by using the regularized Bayesian technique.

Third, the short-run and long-run price transmission parameters of equation (2) are estimated by

implementing restricted maximum likelihood method.

Compared to maximum likelihood method that utilizes maximization, the selection of thresholds

on the basis of RB estimator is done using integral calculus. According to Greb et al. (2014),

integration might be more natural to use in TVECM as it provides a means to account for inherent

variability of the estimates. A function to choose optimal threshold values over the grid of ECTs

is called posterior median and constructed as follows:

∫ 𝑃𝑅𝐵(𝜏𝑖|Δ𝑃, 𝑋)𝑑𝜏𝑖 = 0.5�̂�𝑖𝑅𝐵min (𝛾′𝑃𝑡)

, 𝑓𝑜𝑟 𝑖 = 1,2 (2‘)

where 𝑋 is a 𝑛 × 𝑑 matrix that compactly stacks together columns of ECTs and values of lagged

terms. 𝑃𝑅𝐵(𝜏|Δ𝑃, 𝑋) is well defined across the space of all possible threshold parameters Τ =

{τ1,τ2|min (𝛾′𝑃𝑡) < 𝜏1 < 𝜏2 < max (𝛾

′𝑃𝑡)}. In the previous expression, τ1 and τ2 are optimal

thresholds that separate the space into three regimes and satisfy τ1 < 0 < τ2. Computation is

based on a prior 𝑃𝑅𝐵(𝜏|𝑋) ∝ 𝐼(𝜏 ∈ 𝑇) for 𝜏, where 𝐼(∙) is an indicator function providing

switching between regimes.

Upon identification of the optimal thresholds, the additional parameters of the TVECM are

estimated. We use the restricted maximum likelihood framework implemented as a part of mixed-

effects modeling in R. Each regime is estimated independently, given the values of thresholds

(Gałecki and Burzykowski, 2013).

4.2 Identification of the determinants of market integration

Having completed price transmission analysis, next we combine price transmission elasticities

with various market characteristics in reduced-form regression analysis to identify causes of the

differences in the degree of market integration. We posit that distance and orientation of wheat

production region on export have a significant impact on the degree of market integration.

We conduct econometric analysis using Tobit model, which is fitted to the sample containing data

on regional market pairs in Russia and the USA. Model is given in the following reduced-from

equation:

𝜳𝑖 = ϑ0 + ϑ2𝓓𝑖 + ϑ3𝓧𝑖 + λ0𝓡𝑖 + 𝜆2𝓓𝑖𝓡𝑖 + 𝜆3𝓧𝑖𝓡𝑖 + 𝝃𝑖 (3)

Where 𝜳𝑖 is an estimate of the long-run price transmission elasticity from cointegration equation

(1) in Russia and the USA. 𝓓𝑖 measures average distance in kilometers between different

economic regions in Russia and between states in the USA. 𝓧𝑖 is an indicator variable and takes

value 1 if a region is an exporter to the world market, and equals to 0 otherwise. 𝓡𝑖 is a dummy

variable that equals to 1 if a observation refers to the markets in Russia and 0 – if in the USA. By

introducing interaction terms with country dummy variable 𝓡𝑖, we test conditional hypothesis

that market characteristics have different effect on market integration in Russia compared to the

USA.

5 Data sets and data properties

To estimate our price transmission model, we use a unique dataset of weekly prices of wheat of

class three (Ruble/ton), the most widely traded type of wheat for human consumption in the

Russian domestic market. This data is collected by the Russian Grain Union and is not publicly

available. The quoted prices are paid by traders to farmers on the basis of ex-works contracts. Our

9

data set comprises regional data for the six economic grain producing regions North Caucasus,

Black Earth, Central, Volga, Urals and West Siberia and contains 468 observations (January 2005

until December 2013) (Figure 3). From this database, we construct 15 market pairs in total by

combining each market with all other five regional markets in Russia.

Figure 3: Development of regional wheat prices in Russia in 2005-2013

Note: The area with dashed line on the graph covers the period of export tax (Nov 2007 - May 2008) and export ban

(Aug 2010 - Jul 2011).

Source: Own illustration, data: Russian Grain Union (2014), GTIS (2013).

In addition, we use weekly amounts of grains transported by train between all grain producing

regions of Russia as a measure for interregional grain trade flows (source: Rosstat 2015). This

data is used as additional information to build the model framework and to interpret results. As an

example, Figure 4 gives the price relationship between the regions North Caucasus and Volga as

well as North Caucasus and West Siberia (2007-2013) and the corresponding interregional grain

trade flows transported by train.

However, Figure 4 makes evident that the regional price relationships are not stable, but rather

differ from marketing year to marketing year. In particular, the price of North Caucasus is in some

period higher and in other periods lower than in the other regions. Also, the interregional trade

flows are highly volatile. This implies that the interregional price relationships, which are

depicted in the price transmission model, are highly unstable, and thus parameter estimates may

also not be constant. To tackle this issue, we estimate the price transmission model based on one

marketing year only which is characterized by relatively stable price relationships.

Figure 4: Regional wheat price relationships and interregional trade flows

0

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Mar

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00

t

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B/t

Wheat export Central Black Earth North CaucasusVolga Urals West Siberia

10

Sources: Own illustration, data: Russian Grain Union (2014).

In particular, to assess strength of market integration in Russia, we use regional price observations

of the marketing year 2009/10, when trade was freely possible, as our data base. Also, to

investigate the impact of the drought and the export ban, we estimate the price transmission model

based on the price data for the marketing year 2010/11 and compare the parameter estimates with

those obtained based on the 2009/10 price data. Both data sets comprise 52 observations each.

We use estimate long-run price transmission coefficients from free-trade regime in 2009/10 as a

dependent variable to identify determinants of market integration on the next stage. In addition,

we employ equivalent state-level corn prices for 16 states observed between marketing years 2008

and 2011 (source: USDA AMS, 2016) to get an estimate of long-run price transmission

coefficients for markets in the USA. Each price series contain 156 observations on the weekly

basis. Overall, this dataset generates 63 market pairs, which we construct by pairing 7 markets

from the major producing ‘Corn Belt’ area states with the other 9 markets mostly from net-

consumer states.

In order to identify determinants of the market integration, we supplement our dataset with the

weekly amounts of grains transported by train between all grain producing regions of Russia as a

measure for interregional grain trade flows (source: Rosstat, 2015).

Except for identifying how various market characteristics affect market functioning in Russia and

the USA, we also conduct comparative price transmission analysis to evaluate how markets

function in Russia compared to the USA, which serves as a benchmarch for the most efficient

grain market in our analysis.

Comparative price transmission analysis is conducted based on within-regional price

relationships. We select North Caucasus and West Siberia as a unit of analysis in Russia (source:

Ministry of Agriculture of Russia, 2016) and compare it with the USA markets in Iowa and North

Carolina. Figure 5 shows price developments in local markets of North Caucasus and West

Siberia.

Figure 5: Development of selected regional wheat prices in North Caucasus and West

Siberia in Russia (a), and Iowa and North Carolina, in the USA (b)

a) Russia

0

20

40

60

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10000

15000

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-07

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North Caucasus & West Siberia

Exports West Siberia to North Cauc.

Price North Caucasus

Price West Siberia

-40

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North Caucasus & Volga

Exports North Cauc. to VolgaExports Volga to North Cauc.Price North CaucasusPrice Volga

11

b)

Note: Prices are by-weekly in Russia and daily in the USA. For graphical representation we select only four market in

each region.

Sources: Own illustration, data: Ministry of Agriculture of Russia (2016), GeoGrain and Nick Piggott, 2016. Own

illustration.

North Caucasus is production region which has good access to the ports and is very active on the

world market, while West Siberia – another wheat production region – is mainly active on

domestic wheat trade due to its large distances and geopraphical separation from the world

markets (Figure 6).

Figure 6. Map of crop-growing regions in North Caucasus and West Siberia

0

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Agygeya KrasnodarRostov Stavropol

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West Siberia

Altai Kemerovo

Novosibirsk Omsk

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/t

Iowa

Clinton Eddyville

Emmetsburg W. Burlington

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/t

North Carolina

Candor Cofield

Creswell Laurinburg

12

Source: Own illustration.

We include price series from four winter wheat growing areas in the North Caucasus region such

as Agygeya, Krasnodar, Rostov and Stavropol. In general, price data for local markets in this

region is available from January, 2010 to September, 2016 and consists of 161 by-weekly

observations in total. Exception is Agygeya with its price series starting from June, 2013 and

accounting for 79 observations altogether.

West Siberia, which is the leading spring wheat production region in Russia and advanced grain

milling facilities, is represented by six local markets in this study. Out of them, Novosibirsk,

Altai, Omsk and Tyumen are categorized as production regions and Tomsk and Kemerovo are

net-consuming regions. Price series for Altai, Tomsk and Tyumen markets are fully available

from January, 2010 to September, 2016 including 161 by-weekly observations each. However,

other price series are given for shorter time period. More specifically, Omsk price series start in

December, 2010 generating 139 observations for this market. Price series for Novosibirsk market

starts in July, 2011 (125 observations) and for Kemerovo in September, 2012 (97 observations).

Comparable set of the data is constructed for the USA based on the prices observed in Iowa and

North Carolina (Figure 5) (source: GeoGrain and Nick Piggott, 2016). We use daily prices of the

same time period for all price series from July, 2010 to June, 2012 (506 observations each)

collected in eight markets in Iowa (Cedar Rapids, Clinton, Davenport, Eddyvile, Emmetsburg,

Keokuk, Muscatine and West Burlington) and six markets in North Carolina (Candor, Cofield,

Cresswell, Laurinburg, Roaring River and Statesville).

Before we begin with the price transmission analysis we test the properties of our price series.

Results of the Augmented Dickey-Fuller (ADF) test (Dickey and Fuller, 1981) for a unit root

(Appendix, Table A1) suggest that the all price series used in this study are integrated of order 1.

Further, we apply two different testing techniques to explore the potential of non-linearity in the

market price pairs and to confirm the use of TVECM. Hansen and Seo (2002) provide a test to

check the validity of linear co-integration under the null versus the presence of non-linear co-

integration in a two-regime TVECM with 1 threshold as the alternative. Larsen (2012) provides

an extension to the Hansen and Seo (2002) test by allowing for non-linear co-integration within a

three-regime TVECM with 2 thresholds under the alternative hypothesis.

13

Results of the two tests are given in Table A2 in the Appendix. Both Hansen and Seo (2002) and

Larsen (2012) test results suggest that the null hypothesis can be rejected at 10% level of

significance agains threshold cointegration almost in all cases. Exceptions are three market pairs

in Russia for the marketing year 2010/11 (out of 15), six matket pairs in Iowa (out of 27), and two

market pairs in North Carolina (out of 15). Overall, threshold cointegration is supported for all 15

wheat price pairs of Russia in 2009/10 by at least by one of threshold cointegration test, and for

all 6 price pairs in North Caucasus and 15 price pairs in West Siberia.

Therefore, since in the vast majority of cases test results of Hansen and Seo (2002) and Larsen

(2012) suggest threshold cointegration, we consider these results as strong evidence for the

existence of threshold effects. We explicitly account for threshold effects in the price transmission

analysis by choosing a 3-regime-TVECM for our analysis of price transmission between the

Russian regional wheat markets.

6 Results

6.1 The influence of the export ban 2010/11

The unusually low harvest in the key crop growing areas affected by the drought in 2010 induced

Russian government to impose an export ban on wheat on August 15. Initially, the ban was

introduced to last until December 2010, but it was subsequently prolonged to last until July 2011.

The measure had a profound impact on regional wheat trade in Russia. In particular, North

Caucasus could no longer supply to the world market and was forced to supply wheat

domestically instead. Table 3 shows that North Caucasus directed its flows to the markets which

suffered the most from the harvest failure, specifically Black Earth, Central, Volga and Urals.

This explains the observed wheat trade reversal, e.g. between North Caucasus and Volga region.

West Siberia was less affected by the drought and also supplied wheat to the domestic grain

producing regions which turned into deficit regions in 2010/11, in particular Volga and Urals.

Table 3. Interregional grain trade quantities by train, 2010/11

from

to

North

Caucasus

West

Siberia

Black

Earth Central Volga Urals

Regional trade (in t)

North Caucasus -2,494,506

534,336 1,205,324 453,936 300,910

West Siberia -1,180,827 73,107 101,444 1,006,276

Total imports 534,336 1,278,431 555,380 1,307,186 Source: Götz et al. 2016

To foster interregional grain trade during the export ban, the Russian government introduced

transport subsidy for grain producers located in North Caucasus starting from September 20,

2010. For example, Russian Railways cut delivery fees by half for dispatches heading from North

Caucasus towards the regions of Volga, North West and Central. The given subsidy was valid for

all grain supplies exceeding 300 kilometers and was removed together with the export ban in July

2011.

Even though railway tariff rates halfened during the export ban 2010/11, availability of trucks for

grain transportation was limited as railways were heavily involved in the construction of sport

facilities for the winter Olympic games in Sochi. Moreover, the volume of grain exported by

North Caucasus to other domestic regions was extremely high and even exceeded the availability

of trucks (Gonenko, 2011).

6.1.1 Parameters of the long-run price equilibrium regression

In this section, we discuss estimation results of price transmission analysis for Russia for the

marketing year 2009/10, when trade was freely possible, and in the marketing year 2010/11, when

Russian government imposed export ban. Table 4 presents the parameter estimates of the long-run

14

price equilibrium regression. For the marketing year 2009/10 results suggest that the long-run

price transmission parameter decreases and the intercept parameter increases with increasing

distance between the regions. This corresponds with the Law of One Price according to which

markets are perfectly integrated if the intercept of the long-run price equilibrium is equal to zero

and the slope parameter is equal to one.

Table 4: Parameters of the long-run price equilibrium regression, 2009/10 and 2010/11

Price pairs Long-run price

transmission elasticities

Intercept

parameter

Dependent

variable

Independent

variable

Distance

(km) 2009/10 2010/11 % change 2009/10 2010/11

Central Black Earth 526 0.940 0.917 -2 0.519 0.733

Central Volga 801 0.698 0.824 18 2.525 1.538

Central Urals 2044 0.432 0.670 55 4.699 2.590

Central West Siberia 3346 0.358 0.589 65 5.346 3.654

North Caucasus Black Earth 870 0.333 0.573 72 5.672 3.646

North Caucasus Central 1300 0.346 0.642 86 5.557 3.037

North Caucasus Volga 1708 0.267 0.543 103 6.225 3.896

North Caucasus Urals 2682 0.156 0.443 184 7.132 4.752

North Caucasus West Siberia 3984 0.132 0.392 197 7.340 5.262

Black Earth Volga 1035 0.740 0.890 20 2.153 0.959

Black Earth Urals 2027 0.469 0.760 62 4.366 2.052

Black Earth West Siberia 3329 0.388 0.636 64 5.071 3.248

Volga Urals 1235 0.677 0.844 25 2.645 1.326

Volga West Siberia 2537 0.571 0.717 26 3.575 2.553

Urals West Siberia 1310 0.833 0.834 0 1.452 1.590

Note: All parameters are significant at a level lower than 1%.

Source: Own estimations.

In particular, long-run price transmission is strongest between the neighbouring regions Central

and Black Earth (0.940), the first of which is the major consumption centre and the second is an

important production region, and lowest between North Caucasus and West Siberia (0.132), the

two grain producing regions which are the most distant to each other. One exception is the price

pair North Caucasus-Central, which is integrated slightly stronger than the price pair North

Caucasus-Black Earth, although Central is more distant to North Caucasus than Black Earth. The

strong integration can be explained by the regions’ trade position. Central and North Caucasus are

both the largest importing regions of Russia and strongly competing for grain imports from other

regions of Russia. Though, Central region is the main grain consuming region of Russia whereas

North Caucasus is the primary grain exporting region.

Further, it becomes evident that neighboring regions are stronger integrated than regions which

are not directly adjacent to each other. In particular, besides Central-Black Earth, Central-Volga,

Black Earth-Volga, Volga-Urals and Urals-West Siberia are the regions which exhibit

significantly stronger long-run price transmission elasticity compared to non-neighboring regions.

Our results suggest that North Caucasus is the grain producing region which is the least integrated

with the other grain producing regions of Russia. North Caucasus is the only major grain

producing region with direct access to the world grain market. Thus, different to the other grain

producing regions, North Caucasus is also strongly influenced by the world market conditions

explaining its rather low integration in the Russian regional grain markets.

For the marketing year 2010/11, when several regions experienced severe droughts, and exports to

the world market were forbidden by an export ban, the slope coefficient increases and the

intercept parameter decreases compared to 2009/10 for 13 out of the 15 price pairs. The two

exceptions are the neighboring regions Central-Black Earth and Urals-West Siberia, for which the

long-run price transmission parameter (almost) remains constant. Obviously, the domestic

15

Russian grain market is characterized by stronger market integration during the export ban. This

can be explained by two factors. First, due to the export ban, the influence of the world market

conditions on domestic price formation decreases particularly in those regions, which are usually

involved in grain export to the world market. Thus, the influence of the common domestic factors

increases, particularly in the export-oriented regions which strengthens their integration in the

domestic market. This is also reflected in the increase in the long-run price transmission

parameter (in percentage), which is strongest for the price pairs involving North Caucasus, the

increase varying between about 70% and 200%. Second, due to the severe harvest shortfalls of up

to 60% in some regions in 2010/11, interregional trade flows increase strongly and are observed

from the surplus regions North Caucasus and West Siberia to the deficit regions (compare Table

3), contributing to the strengthened domestic market integration. This rise in the domestic grain

trade was fostered by the implementation of the wheat export ban.

6.1.2 Estimated parameters of the TVECM

Selected parameters of the 3-regime TVECM, which is estimated for the 15 market pairs

separately for the marketing years 2009/10 and 2010/11 are presented in Tables 5a and Table 5b.

It becomes evident that the vast majority of observations are attributed to the middle regime for

12 out of 15 regional price pairs in 2009/10. For example, for the price pair Central–Black Earth,

40 observations are assigned to the middle regime, whereas 7 observations belong to the lower

and one observation to the upper regime. This means that the error correction term between

regional market pairs is usually smaller than the absolute value of the lower and upper threshold,

providing evidence for strong market integration. In 2010/11 the number of market pairs for

which the majority of observations lays in the middle regime increases to 14 out of the 15 market

pairs. This can be interpreted as evidence of the strengthened integration of regional markets

during the export ban.

Table 5a. Results of TVECM: Russia 2009/10

Price pair Lower regime Middle regime Upper regime Total adjustment

Number of obs.

Dependent – indep. variable Rho1 Pvalue Lower

Thresh.

Rho2 Pvalue Upper

Thresh.

Rho3 Pvalue Lower Middle Upper Band of

inaction

1 Central - Black Earth -0.212 0.360 -0.021 -0.208 0.336 0.018 -0.353 0.089 0.340 0.364 0.733 0.039

Black Earth - Central 0.340 0.072 0.364 0.035 0.380 0.015 7 40 1

2 Central - Volga -0.100 0.291 -0.013 -0.207 0.337 0.003 -0.147 0.168 - - - 0.016

Volga - Central 0.121 0.264 -0.180 0.408 -0.081 0.494 17 12 19

3 Central -Urals -0.029 0.757 -0.047 -0.149 0.259 0.029 -0.173 0.030 0.310 - 0.173 0.076

Urals - Central 0.310 0.004 0.179 0.214 0.100 0.233 17 18 13

4 Central - West Siberia -0.039 0.646 -0.062 -0.102 0.311 0.021 -0.166 0.014 0.260 - 0.166 0.083

West Siberia - Central 0.260 0.041 0.082 0.574 -0.005 0.955 12 17 19

5 North Caucasus - Black Earth -0.207 0.041 -0.021 -0.207 0.041 0.020 -0.207 0.041 0.207 0.207 0.207 0.041

Black Earth - North Caucasus -0.018 0.809 -0.018 0.809 -0.018 0.809 14 16 18

6 North Caucasus - Central -0.300 0.025 -0.030 -0.216 0.088 0.020 -0.168 0.136 0.300 0.216 - 0.050

Central - North Caucasus -0.152 0.187 0.114 0.299 -0.031 0.744 7 24 16

7 North Caucasus - Volga -0.167 0.078 -0.038 -0.177 0.136 0.012 -0.153 0.060 0.167 - 0.153 0.050

Volga - North Caucasus -0.107 0.276 -0.074 0.569 -0.091 0.328 4 26 18

8 North Caucasus - Urals 0.041 0.684 -0.036 -0.029 0.820 0.024 -0.064 0.379 - - - 0.060

Urals - North Caucasus 0.176 0.132 0.154 0.284 0.081 0.360 11 21 16

9 North Caucasus - West Siberia -0.116 0.146 -0.049 -0.125 0.036 0.029 -0.125 0.036 - 0.125 0.125 0.078

West Siberia - North Caucasus -0.010 0.926 0.057 0.573 0.057 0.573 6 29 13

10 Black Earth - Volga -0.094 0.086 -0.046 -0.146 0.052 0.011 -0.094 0.086 0.094 0.146 0.094 0.057

Volga - Black Earth 0.022 0.781 -0.003 0.979 0.022 0.781 8 26 14

11 Black Earth - Urals 0.063 0.318 -0.059 0.063 0.318 0.031 0.005 0.928 0.295 0.295 0.193 0.090

Urals - Black Earth 0.295 0.000 0.295 0.000 0.193 0.016 10 28 10

12 Black Earth - West Siberia -0.007 0.898 -0.087 -0.069 0.208 0.025 -0.049 0.375 - - - 0.112

West Siberia - Black Earth 0.106 0.229 0.015 0.859 0.016 0.849 6 26 16

13 Volga - Urals -0.160 0.203 -0.058 -0.019 0.858 0.038 -0.297 0.014 0.210 0.200 0.297 0.096

Urals - Volga 0.210 0.067 0.200 0.043 0.120 0.245 8 33 7

14 Volga - West Siberia -0.141 0.274 -0.056 -0.201 0.035 0.035 -0.288 0.004 - 0.201 0.288 0.091

West Siberia - Volga 0.216 0.125 0.098 0.228 -0.026 0.763 4 38 6

15 Urals - West Siberia -0.206 0.072 -0.027 -0.186 0.183 0.012 -0.206 0.141 0.206 - - 0.039

West Siberia - Urals 0.213 0.157 0.167 0.324 0.011 0.951 11 22 15

17

Table 5b. Results of TVECM: Russia 2010/11

Price pair Lower regime Middle regime Upper regime

Total adjustment

Number of obs.

Dependent – indep. variable Rho1 Pvalue Lower

Thresh. Rho2 Pvalue

Upper

Thresh. Rho3 Pvalue Lower Middle Upper

Band of

inaction

1 Central - Black Earth 0.018 0.964 -0.022 -0.437 0.096 0.014 -0.272 0.369 0.587 0.437 - 0.036

Black Earth - Central 0.587 0.098 0.022 0.915 0.301 0.243 6 36 6

2 Central - Volga -0.690 0.005 -0.018 -0.290 0.161 0.008 -0.168 0.334 0.690 - - 0.026

Volga - Central -0.142 0.568 0.117 0.566 0.178 0.292 8 27 13

3 Central -Urals -0.457 0.000 -0.095 0.042 0.524 0.058 -0.039 0.826 0.457 - 0.304 0.153

Urals - Central -0.017 0.873 0.084 0.171 0.304 0.078 3 41 4

4 Central -West Siberia -0.329 0.007 -0.105 0.118 0.061 0.054 0.158 0.131 0.329 -0.118 0.274 0.159

West Siberia - Central 0.040 0.772 0.028 0.764 0.274 0.042 3 38 7

5 North Caucasus - Black Earth -0.244 0.054 -0.090 -0.264 0.035 0.038 -0.217 0.121 0.244 0.264 - 0.128

Black Earth - North Caucasus -0.014 0.846 -0.075 0.171 0.008 0.921 2 38 8

6 North Caucasus - Central -0.239 0.010 -0.032 -0.385 0.397 0.004 -0.242 0.009 0.129 - 0.129 0.036

Central - North Caucasus -0.110 0.094 0.308 0.154 -0.113 0.089 16 14 18

7 North Caucasus - Volga -0.308 0.049 -0.046 -0.315 0.075 0.007 -0.260 0.066 0.054 0.315 0.103 0.053

Volga - North Caucasus -0.254 0.009 0.033 0.748 -0.157 0.042 10 23 15

8 North Caucasus - Urals -0.323 0.002 -0.099 -0.323 0.002 0.085 -0.328 0.098 0.323 0.323 0.328 0.184

Urals - North Caucasus -0.036 0.365 -0.036 0.365 -0.149 0.210 4 40 4

9 North Caucasus - West Siberia -0.381 0.000 -0.053 -0.370 0.011 0.038 -0.453 0.003 0.381 0.370 0.453 0.091

West Siberia - North Caucasus -0.048 0.536 0.013 0.921 -0.134 0.335 10 29 9

10 Black Earth - Volga -0.139 0.371 -0.029 -0.139 0.404 0.008 -0.126 0.401 - - - 0.037

Volga - Black Earth 0.012 0.948 -0.056 0.766 -0.008 0.963 6 22 20

11 Black Earth - Urals -0.271 0.011 -0.103 0.020 0.780 0.076 -0.322 0.003 0.271 - 0.322 0.179

Urals - Black Earth -0.063 0.500 0.039 0.518 -0.123 0.184 2 44 2

12 Black Earth - West Siberia -0.246 0.008 -0.107 0.041 0.430 0.071 -0.063 0.657 0.246 - - 0.178

West Siberia - Black Earth -0.150 0.186 0.104 0.126 0.003 0.984 2 44 2

13 Volga - Urals -0.194 0.027 -0.107 -0.092 0.163 0.069 -0.225 0.027 0.194 - 0.225 0.176

Urals - Volga -0.018 0.812 0.015 0.791 -0.043 0.624 2 43 3

14 Volga - West Siberia -0.104 0.170 -0.105 0.041 0.529 0.046 0.105 0.439 - - 0.418 0.151

West Siberia - Volga 0.032 0.679 0.061 0.376 0.418 0.005 4 37 7

15 Urals - West Siberia 0.053 0.513 -0.061 0.039 0.619 0.029 0.039 0.619 0.318 0.300 0.300 0.090

West Siberia - Urals 0.318 0.012 0.300 0.020 0.300 0.020 3 36 9

Note: Total adjustment in one regime is calculated as the sum of the absolute value of the respective regime-specific speed of adjustment parameters of the TVECM. The band of

inaction is given as the difference between the absolute value of the upper and lower threshold.

Source: Own estimations.

Another attribute to characterize market integration is the size of the band of inaction, difference

between the absolute value of the upper and lower threshold. The average size of the band of

inaction is significantly lower in the marketing year 2009/10 amounting to 0.07 compared to the

marketing year 2010/11 amounting to 0.12. For both marketing years the band of inaction is

highest for all price pairs which include either Ural or West Siberia, two peripheral regions which

are characterized by large distance to the grain consuming and exporting regions and thus high

trade costs. Though, the band of inaction is rather low for the price pair Urals-West Siberia, which

are neighbouring regions and are characterized by strong integration.

All price relations between the given regional markets are characterized by a positive and a

negative threshold. For example, for the market pairs containing the Central region, the threshold

with a positive value refers to the trade costs of wheat supplied to the Central region, whereas the

negative threshold corresponds to trade costs of wheat originating in the Central region and

exported to the respective partner region. As it was explained in section 2, it should be pointed out

that Central is the pivotal region representing the largest wheat consuming region of Russia, while

the other regional markets (Black Earth, West Siberia, Urals and Volga) are the primary suppliers

of wheat to Central.

Estimates of the threshold parameters in 2009/10 generally confirm the influence of distance. For

example, for all price pairs which include the Central market, the absolute value of the negative

threshold increases with distance (compare Figure 1). In particular, the absolute value of the

identified negative threshold is highest for the market pair Central-West Siberia (0.062), two

markets which are the most far apart, while it is significantly lower for the price relationship

between the markets Central and Black Earth (0.021) and Central and Volga (0.013) which are

each neighbouring regions. Thus, parameter estimates indicate that it is almost three times costlier

to supply wheat from West Siberia to Central, than from Black Earth to Central. The threshold is

second highest for the market pair Central-Urals (0.047) which is in line with the actual distance

between those markets.

A similar pattern is observed for all price pairs involving the region North Caucasus. The absolute

value of the negative threshold is lowest for the neighbouring regions North Caucasus and Black

Earth (0.021) and is highest for the most distant regions North Caucasus and West Siberia (0.049).

Generally, all price pairs including Urals or West Siberia as a region are characterized by

relatively large thresholds, which can be explained by their peripheral location and the high

transaction costs involved.

The increase in the band of inaction in 2010/11 compared to 2009/10 can be explained by the

increase of the size of thresholds. Parameter estimates suggest that the size of thresholds had

increased compared to 2009/10 for the vast majority of price pairs. The lower threshold increased

for all price pairs except one. The identified upper threshold increased for 11 out of the 15 price

pairs. These results suggest that interregional trade costs increased in 2010/11 compared to

2009/10.

Information provided by the Russian Grain Union confirms these results. First, the railway

transport costs were increased by 10% by the government in 2010/11 compared to 2009/10.

Further, the destinations of interregional grain trade flows changed during the export ban and

grain trade flows were even reversed. Traders had to extend their business to other regions and

could not make use of their established business contacts. Thus, transaction costs of trade

increased strongly by increasing trade risk associated with a high level of fraud and high risk of

contract enforcement.

The influence of distance is also reflected in the size of the regime-specific speed of adjustment

parameters and the regime-specific total adjustment. Total adjustment in one regime is calculated

as the sum of the absolute value of the respective regime-specific speed of adjustment parameters

of the TVECM. In the following we focus on the parameters which are statistically significant at

least at the 10% level, and which are of the expected sign.

19

Among the 15 price pairs, the speed of adjustment parameter is highest for the neighbouring

regions Central-Black Earth amounting to 34% to 73% per week in 2009/10 in the lower and

upper regime, respectively. The size of the speed of adjustment decreases to 31% for the price

pairs Central-Urals to 26% for Central-West Siberia, reflecting the influence of distance. The

speed of adjustment parameters observed for price pairs involving North Caucasus are

significantly lower. In particular, the highest speed of adjustment parameter is observed for the

price pair North Caucasus-Central amounting to 30%. The regime-specific parameters are

significantly lower for the price pairs North Caucasus-Black Earth, North Caucasus-Urals and

North Caucasus-West Siberia, and are decreasing with increasing distance between the regions

from 21% (North Caucasus-Black Earth) to 13% (North Caucasus West Siberia).

The influence of trade costs is also reflected when comparing the regime-specific speed of

adjustment parameters and the total adjustment for each price pair. We find 8 price pairs for

2009/10 and 12 price pairs for 2010/11 out of the 15 price pairs each for which the speed of

adjustment parameters and the total adjustment is higher in at least one of the outer regimes

(lower and upper regime) compared to the middle regime. This confirms the theory underlying

threshold models applied in spatial price transmission, according to which the speed at which

deviations from the long-run price equilibrium are corrected, is higher if the price deviations

exceed the trade costs.

The regime-specific speed of adjustment parameters are increasing for at least one regime in 13

out of 15 cases in 2010/11 compared to 2009/10, confirming once again that the integration of the

regional wheat markets was strengthened during the export ban.

6.2 Comparison with the corn market in the USA

To assess how well the regional wheat markets functions in Russia, we conduct an analysis of the

integration of the corn markets in the main grain producing regions of the USA. The corn market

of the USA seems is particularly suitable for comparison since it is characterized by rather high

variation in the level of production and strong domestic trade flows to balance supply and demand

of corn. We assume that the corn market of the USA is one of the most efficient grain markets in

the world characterized by well-developed transport and storage infrastructure and high market

transparency, serving as a benchmark for assessing the efficiency of the Russian wheat market in

this study.

Corn is the primary grain produced in the USA, accounting for more than 80% of total grain

production (USDA NASS, 2016). For comparison, wheat has a 60% share in total grain

production in Russia. The majority of corn is grown in the so-called “corn belt” region ranging

over the states Iowa, Illinois, Nebraska, Minnesota, Indiana, South Dakota, Kansas, Ohio and

Missouri and accounting for about 80% of total corn production of the USA. Similar to Russia,

the size of corn production in the USA is characterized by large regional fluctuations (Table 5).

For example, corn production in Illinois varied between 65% in 2012 and 132% in 2014 of the

average corn production of the previous 3 years. Nonetheless, the volume of harvested corn is

quite stable on the national level.

Table 5: Corn production developments in the states of the “corn-belt” area of the USA

(2004-2015), in % of the average of previous 3 years

State 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Iowa 122 106 98 110 100 111 92 104 81 101 111 118

Illinois 126 95 97 122 110 99 90 95 65 122 132 105

Nebraska 124 113 95 117 107 117 99 104 85 113 108 113

Minnesota 119 114 101 101 103 109 108 97 110 100 91 111

Indiana 121 114 97 111 97 104 97 93 67 133 132 91

South Dakota 147 111 65 123 133 150 93 105 83 137 119 113

Kansas 133 137 86 123 111 134 109 81 70 107 127 120

Ohio 126 114 98 114 86 114 106 102 85 131 114 88

Wisconsin 97 116 104 112 93 109 117 116 82 93 107 111

20

Missouri 150 94 99 118 100 111 86 88 64 135 183 100

USA total 124 108 96 117 105 111 98 98 85 117 115 105

Note: USA total considers corn production in all states.

Source: Own illustration, data: USDA NASS, 2017.

Moreover, USA is the world’s largest exporter of corn occupying 35%-40% of total corn exports

world-wide (USDA WASDE, 2017). Nonetheless, 80-90% of the corn production in the USA is

supplied to the domestic market (USDA NASS, 2017). due to the rapid expansion of biofuel

production, industrial use of the domestically produced corn has substantially increased. Even

more, since 2010 amount of corn consumed by energy sector is exceeding the quantity of corn

that is used as an animal feed. As of 2014, biofuel plants account for the 40% of total domestic

corn usage (USDA ERS, 2017). Another characteristic of the USA grain market that structurally

distinguishes it from Russia is that corn processing facilities are concentrated in the production

areas. In order to ensure logistical efficiency ethanol plants are usually established close to the

grain elevators, from where corn is primarily transported by trucks to the processing facilities.

Corn transportation in the USA is based on trucks, rails and barges (Sparger and Marathon, 2015).

On average 80% of domestic corn transfers is performed by trucks since it is the most cost

advantageous means of transportation on shorter distances (less than 500 kilometers). The rest of

the domestic corn hauling on longer distances is conducted by rails. Corn for export is mainly

transported by waterway transport. Barges are primarily busy for deliveries to the port export

terminals. They transport around 70% of totally exported corn from the “corn belt” southwards to

the Mississippi Gulf ports (FAPRI-UMC 2004), which by itself is one of the leading port for corn

export accounting for 65% of total corn exports (USDA Federal Grain Inspection Service grains

inspections, 2013, as cited in Denicoff et al., 2014). The remaining 30%-40% of the exported corn

is hauled by rail predomionantly to the Pacific Northern harbors in the Washington state.

Figure 9 illustrates cost advantages of selected transportation modes in the USA depending on the

distance covered and compares it with the railway tariff rates in Russia. Trucks are the cheapest

transportation mode in the USA within distances less than 500 kilometers. While comparing long-

distance transportation modes, barges are twice as cheap as trains if distance exceeds 1500 km,

however access to this water transport emtirely depends on the geographic proximity of a corn

trade facilities and its access to a river.

Figure 9 also shows railway tariff rates of wheat transportation in Russia to compare it with the

rates in the USA. Railway tariff rates to transport 1 tonnes of wheat in Russia are almost

undifferenciable from the rates given for corn transportation in the USA on comparable distances.

However, critical dependence on the railway infrastructure largely differs between countries.

While grain transportation over large distances solely depends on the only stated-operated railway

company in Russia, low-cost barges and several railroad companies provide corn cargo

transportation in the USA.

Figure 9. Estimated fees of grain deliveries in the USA and Russia by different modes of

transportation

0

15

30

45

60

75

0 500 1000 1500 2000 2500 3000 3500 4000

Gra

in d

eliv

ery

fees

(U

SD/t

)

Distance (km)

Comparision of unit transportation costs: Russia and the USA

Truck (USA)

Barge (USA)

Rail (USA)

Rail (Russia)

21

Notes: Barge rates represent spot shipping costs towards the southbound direction along the Mississippi River to the

ports of New Orleans from 7 different origin locations (Twin Cities, minnessota; Mid-Mississippi; Lower Illinois

River; St. Louis; Cincinnati; Lower Ohio; Cairo-Memphis) on October 05, 2010 when trade capacity and

correspondingly, railway rates are at their peak. Distance for barge rates is calculated based on National Water

Information System (2016). Rail tariff is estimated on October 01, 2010 corresponding to the peak transportation

season. Distance for Rail rates is calculated based on BNSF (2016) railway distance calculator. Truck rates are

estimated for the 3th

quarter in 2010 based on three different levels of truck rates that depend on the length of

distance: 4.15 USD per mile if distance is at most 25 miles, 2.4 USD per mile if distance is at most 100 miles, and

2.28 USD per mile if covered distance is 200 miles. Rates are based on trucks with 80 000 lbs gross vehicle weight

limit. Estiamted volume per truck is 25 metric tones.

Sources: Own illustration, data: Rosstat 2015 and USDA AMS, 2017.

6.2.1 Parameters of long-run price equilibrium relationship and TVECM estimates

Compared to Russia, transportation logistics function more efficiently and delivery costs are much

lower in USA. The influence of trade costs are usually reflected in the strength of market

integration between spatially separated regions which decrease with increasing distance between

the markets.

Table A3 and A4 (a, b, c and d) in the Appendix respectively shows estimates for the long-run

price transmission elasticities and TVECM parameter estimates for markets in North Caucasus

and West Siberia in Russia, and Iowa and North Carolina in the USA. However, in order to better

visualize comparisions across countries, as well as cross-regional differences within the countries,

we use estimation output from the Tables A3 and A4 and depict all comparisons in boxplots on

Figure 10.

Figure 10: Boxplot comparisons of price transmission coefficients for Russia and the USA

a) Long-run price transmission elasticity b) Speed of adjustment parameter

c) Observations in middle regime (%) d) Band of inaction

e) Upper thresholds f) Lower thresholds

Source: Own illustration.

Analysis shows that overall, markets in Russia and the USA function differently. Long-run price

transmission coefficients are much higher in the USA indicating almost complete transmission of

price shocks compared to Russia (panel a, Figure 10). Median estimate of price transmission

elasticities is 0.81 and 0.84 in North Caucasus and West Siberia, and 0.97 and 0.93 in Iowa and

North Carolina, respectively. In addition, similar to the results of Russian market integration

analysis on the regional level (compare table 4), price transmission coefficients are again more

heterogeneous ranging between 0.53 and 0.92 in Russia, while it has modest variation in the USA

varying between 0.81 and 1.01.

In addition, our results suggest that there are not significant differences in the functioning of grain

markets between West Siberia and North Caucasus. This is surprising as we initially expected to

observe notably lower degree of price transmission within West Siberia since the region is

separated from the world market by large distances. On the contrary, grain in North Caucasus is

only traded to export markets and local farmers have many alternatives to sell their harvest to the

export-oriented traders.

Yet being segregated from the world market, West Siberia is active in domestic wheat trade. From

the The region produces superior quality wheat and also has plentiful elevator facilities,

explaining why markets in West Siberia are functioning equally as well as in North Caucasus. The

23

only market in West Siberia which has low price transmission coefficient with the other markets

is Kemerovo province (Table A3, b in Appendix), which is net-consumer market and is less active

in the regional grain trade compared to other markets in West Siberia.

Further, efficiency of markets in transferring price shocks between regions is reflected in high

speed to correct disequilibrium. As results indicate, eliminating short-run price disequilibrium is

more time-consuming process in Russia compared to the USA (Panel b, Figure 10). In terms of

median values, price disequilibrium between market pairs is almost completely eliminated in the

USA in two weeks (93% Iowa and 89% in North Carolina), while just quarter to third of the

amount adjusted in the USA is corrected in North Caucasus (22%) and West Siberia (32%) during

the same time period.

For comparison, all market pairs in North Caucasus are within the distance interval up to 350

kilometers and show significant speed of adjustment towards equilibrium whereas distance

diapason is larger in West Siberia (from 200 to 1550 km) and market pair Kemerovo-Tyumen

with the largest distance in West Siberia does not show any adjustment at all towards long-run

equilibrium.

Another measure of the market integration is the frequency of instances when error correction

term between market pairs is smaller than the absolute value of the lower and upper threshold,

providing evidence for strong market integration. This is measured by the percent (or,

alternatively, by the number) of the ECT observations that fall in the middle regime (panel c,

Figure 10). Agreeing with the findings of the long-run price transmission analysis, Russian

markets again show lower degree of market integration compared to the USA as the median

percent of observations attributed to the middle regime is respectively 71% and 77% in North

Caucasus and West Siberia, whereas 88% and 82% of ECT values fall in the “band of inaction”

interval in case of Iowa and North Carolina.

Comparison of the band of inaction (difference between the absolute value of the upper and lower

threshold), upper and lower thresholds estimates univocally indicate that trade costs are much

larger in Russia than in the USA (panel d, e and f, Figure 10). Median threshold values vary

between 0.02 and 0.05 Iowa and North Carolina, whereas thresholds are 3-5 times larger in Russia

ranging between 0.10 and 0.14. Furthermore, threshold estiamtions show that trade costs are more

uniformly distributed in the USA and is characterized by higher variability in Russia. Difference

between 75th

and 25th

quartile values which corresponds to the height of shaded area on the

boxplots varies between 0.01 and 0.04 in the USA irrespective of the selected measure of

thresholds (upper, lower or band of inaction values). In contrast, this difference is 0.11, 0.05 and

0.11 in North Caucasus and 0.08, 0.10 and 0.19 in West Siberia for lower thresholds, upper

threshold and band of inaction, respectively. As industry practitioners indicate the key obstacles in

Russia are large distances between markets and logistical challenges that hinder intensive trade

linkages between markets of Russia.

6.2.2 Determinants of market integration and their impact in Russia and the USA

We utilize estimates of the long-run price transmission parameters from the price transmission

analysis for the regonal markets in Russia (Table 4, 2009/10) and the USA (Table A5, Appendix)

to associate it with various market characteristics and identify and compare determinants of

market integration in those countries.

Results of a formal analysis of market characteristics and are summarized in Table 6. The analysis

shows that in Russia distance has negative and statistically significant influence on price

transmission in Russia. Closer markets are more strongly integrated in Russia than markets that

are far away from each other. For instance, if we consider capital city Moscow in the Central

region as a point of reference and compare two markets in terms of proximity to Moscow, then

the one which is located 1000 km closer to the capital city will show greater magnitude of price

transmission by 18.4% than another market which is more distant from Moscow. The impact of

24

distance is less pronounced in the USA. Increase of distance between markets by 1000 km

translates into decreased price transmission coefficient only by 8.6%.

Table 6: Tobit regression results: analysis of the determinants of market integration1

Dependent variable: Parameter estimates

Long-run price transmission elasticity Russia a)

USA b)

Distance 1000 km -0.184*** -0.086***

[0.017] [0.008]

Exporter -0.353*** 0.081***

[0.055] [0.014]

Constant 0.987**

[0.019]

Observations 78

F-test (8, 70) 65.68***

(Prob > F = 0.000)

Note: a)

data sample refers to 2009/10 marketing year when trade was freely possible and includes data on 15 regional

market pairs. b)

data sample refers to 2008/11 marketing year and includes 63 observations.

*, **, *** indicate statistical significance at 10, 5 and 1%, respectively. Robust standard errors in square brackets.

Source: Own estimations.

Further, results indicate that the wheat export region of Russia is only loosely integrated with

other production regions. This is in contrast to the USA where grain prices in the export-oriented

regions strongly influences price discovery in other domestic markets in the USA. North

Caucasus, which accounts for the lion’s share of total Russian wheat export, demonstrates very

low level of market integration (on average by 35.3%) compared to other regions in Russia.

Contrary, if a region exports to the world markets in the USA, this strengthens integration of that

region with the other domestic markets by 8.1%. We interpret this result as an indicator that in the

USA market participants having easier access to the world markets consider price in exporting

region as an opportunity cost and use this information as a reference price to negotiate their own

trade transactions.

7 Conclusions

In this paper we have investigated the regional price relationships between the primary grain

production regions of Russia to assess the efficiency of the Russian wheat market and have

compared them to results for the corn market of the USA.

In general, the results of the price transmission analysis for Russia demonstrate the strong

influence of distance between the grain producing regions on their price relationships. In

particular, the band of inaction and the upper and the lower threshold increase with distance

between the regions of the price pairs, whereas the long-run price transmission elasticity, the

speed of adjustment parameter and the total adjustment decrease with distance. The speed of

adjustment parameters and total adjustment are highest for neighbouring regions.

The results for Russia also indicate high level of variation in the strength of market integration

across regions. Price pairs involving North Caucasus, the exporting region with direct access to

the world markets, are characterized by particularly low long-run price transmission elasticity,

speed of adjustment parameters and total adjustment, demonstrating that the influence of the

world market price is strongest in the exporting region North Caucasus, which reduces its regional

integration in the Russian wheat market. This suggests that the Russian grain market can be

divided in two clusters: the exporting region next to the Black Sea which is strongly influenced by

1 Due to more convenient illustration, we present complete effect of each variable separately for Russia and the USA

in this table instead of showing main effects of the variables for the USA and their incremental effect for Russia

(coefficients on country dummy interaction terms).

25

world market conditions and the other grain production regions which are almost isolated from

the exporting region and also the world market which are mainly influenced by domestic market

conditions.

In a large country like Russia, distance between the grain producing regions has strong influence

on their price relationships. The thresholds are highest for price pairs involving Urals and West

Siberia, reflecting the relatively high trade costs due to the peripheral location of those regions

within the Russian wheat market. This is reflected in a band of inaction and an upper and lower

threshold increasing with distance between the regions, whereas the long-run price transmission

elasticity, the speed of adjustment parameter and the total adjustment decrease with distance.

Thus, we find the highest speed of adjustment parameters and total adjustment for neighbouring

regions.

Our results suggest that the integration of the regional wheat markets strengthened during the

wheat export ban in 2010/11, which can be explained by the increase in interregional trade flows.

In particular, price transmission elasticities and regime-specific speed of adjustment parameters

increased in 2010/11 compared to 2009/10 for many price-pairs. Further, we find that the size of

thresholds and the band of inaction increasing in 2010/11 compared to 2009/10. We trace this

back to increasing transport costs and also increasing trade risk of interregional grain transactions.

The increasing trade risks results from the change in export destinations requiring to involve new

trade partners. Obviously, the transport subsidy was too low to prevent that total transaction costs

of interregional trade increased during the export ban period. These results confirm that in general

the risk of business is particularly high in Russia due to a high degree of fraud and the difficulties

to enforce contracts.

The comparison of the Russian wheat market with the corn market of the USA makes evident that

the efficiency of the Russian wheat market is significantly lower. In particular, the Russian market

is characterized by a high heterogeneity in the degree of price transmission compared to the USA.

Furthermore, TVECM estimations show that thresholds are larger and it takes more time for price

shocks to be corrected in Russia compared to the USA.

Our analysis on the determinants of market integration confirms a lower influence of distance on

market integration in the USA compared to Russia. Further, the exporting region is particularly

strongly integrated with other regions in the USA whereas the integration of the exporting region

in the other domestic markets is particularly low in Russia.

Our study offers several important implications in terms of trade policy and food security. First,

strengthening market integration between the grain production regions could contribute to

decrease price volatility within the regions of Russia. If price signals were faster transmitted from

deficit to surplus regions, and the transaction costs of trade were decreased, incentives for

interregional trade from surplus to the actual deficit regions would be strengthened and contribute

to cushion the price increasing effects of regional production shortfalls.

This in turn would reduce the incentives for the government to implement export controls on grain

market which in the long-run strongly negatively affect the further development of the grain

sector.

Second, the grain export potential in Russia can be increased as long as this results from an

increase in grain production in the exporting region which is well integrated in the world market.

However, the mobilization of grain export potential in other grain production regions will require

substantial investments in grain market infrastructure to improve their integration in the export

market and thus in the world grain market, which might cause substantial additional costs.

Ultimately world market price conditions will determine if this is efficient. As an alternative the

wheat supply chain might be restructured in those regions. Livestock production might settle in

the more remote grain production regions and instead of grains meat and meat products will be

exported to the world market.

26

References

BALCOMBE, K., BAILEY, A. and BROOKS, J. (2007). Threshold Effects in Price Transmission: the Case of

Brazilian Wheat, Maize and Soya Prices. American Journal of Agricultural Economics 89: 308-323.

BAYLIS, K., C. JOLEJOLE-FOREMAN and M. MALLORY (2014): Impact of wheat and rice export ban on

Indian market integration. Contributed Paper, IAMO-Forum 2014 “The rise of the “emerging

economies”: Towards functioning agricultural markets and trade relations?”, June 25-27, Halle

(Saale), Germany.

BNSF (2016): Rail Miles Inquiry, http://www.bnsf.com/

CHAN, K. S. (1993). Consistency and Limiting Distribution of the Least Squares Estimator of a Threshold

Autoregressive Model. The Annals of Statistics, 21: 520-533.

DENICOFF, M. R., PRATER, M. E., and P., BAHIZI (2014): Corn Transportation Profile, USDA Agricultural

Marketing Service, August 2014, http://dx.doi.org/10.9752/TS090.08-2014

DICKEY, D. A. and W. A. FULLER (1981): Likelihood Ratio Statistics for Autoregressive Time Series with

a Unit Root. In: Econometrica 49 (4): 1057-1072.

FACKLER, P. and B. GOODWIN (2001): Spatial price analysis. In: Gardner, B. and G. Rausser (eds.):

Handbook of Agricultural Economics. Elsevier, Amsterdam: 971-1024.

FAPRI-UMC (2004): Grain Transportation and Marketing Channels, FAPRI-UMC Briefing Paper #04-04.

GAŁECKI, A. T. and T. BURZYKOWSKI (2013). Linear mixed-effects models using R: A step-by-step

approach. New York, NY: Springer.

GEOGRAIN (2016): unpublished data, http://geograin.com/

GONENKO, I. (2011). Grain wagons versus auto. Agroinvestor, 10 (45).

GOODWIN, B. K. and N. PIGGOTT (2001): Spatial market integration in the presence of threshold effects.

In: American Journal of Agricultural Economics 83 (2): 302-317.

GÖTZ, L., T. GLAUBEN and B. BRÜMMER (2013): Wheat export restrictions and domestic market effects in

Russia and Ukraine during the food crisis. In: Food Policy 38: 214-226.

GÖTZ, L., I. DJURIC and O. NIVIEVSKYI (2016): Regional price effects of extreme weather events and

wheat export controls in Russia and Ukraine. In: Journal of Agricultural Economics 67 (3): 741-763.

GREB, F., KRIVOBOKOVA, T., MUNK, A. and S. VON CRAMON-TAUBADEL (2014). Regularized Bayesian

Estimation of Generalized Threshold Regression Models. Bayesian Anal. 9, No. 1, 171-196.

GREB, F., S. VON CRAMON-TAUBADEL, T. KRIVOBOKOVA and A. MUNK (2013): The estimation of

threshold models in price transmission analysis. In: American Journal of Agricultural Economics 95

(4): 900-916.

GTIS (2013). Global Trade Atlas, Global Trade Information Services. Unpublished data.

HANSEN, B. and B. SEO (2002): Testing for two-regime threshold co-integration in vector error-correction

models. In: Journal of Econometrics 110 (2): 293-318.

INTERFAX, 2016. Russia & CIS Food and Agriculture Weekly, various issues.

JOHANSEN, S. (1988): Statistical Analysis of Cointegration Vectors. In: Journal of Economic Dynamics

and Control 12 (2-3): 231-254.

LARSEN, B. (2012): A Threshold Cointegration Analysis of Norwegian Interest Rates. Master Thesis.

University of Tromsø.

LIOUBIMTSEVA, E. and G. HENEBRY (2012): Grain production trends in Russia, Ukraine and Kazakhstan:

New opportunities in an increasingly unstable world? In: G.M. Front. Earth Sci. 6 (2): 157-166.

LO, M.C. and E. ZIVOT (2001): Threshold cointegration and nonlinear adjustment to the Law of One Price.

In: Macroeconomic Dynamics 5 (4): 533-576.

MACKINNON, J. G. (1996). Numerical Distribution Functions for Unit Root and Cointegration Tests.

Journal of Applied Econometrics, 11, 601-618.

MINISTRY OF AGRICULTURE OF RUSSIA (2016): Специализированный Центр Учета В

Агропромышленном Комплексе, http://www.specagro.ru/

NATIONAL WATER INFORMATION SYSTEM (2016): NWIS Mapper,https://maps.waterdata.usgs.gov/mapper

OECD/FAO (2012): OECD-FAO Agricultural Outlook 2012-2021. OECD Publishing and FAO.

27

ROSSTAT (2015): Federal State Statistics Service. www.gks.ru.

RUSSIAN GRAIN UNION (2014). Unpublished data.

SCHERBANIN, Y. (2012). Some problems of development of railway infrastructure in Russia (in Russian

lnguage). Problems of Forecasting, 12: 49-62.

SPARGER, A. and N., MARATHON (2015). Transportation of U.S. Grains: A Modal Share Analysis 1978-

2013 update, June 2015. USDA, Agricultural Marketing Service.

STEPHENS, E. C., MABAYA, E., VON CRAMON-TAUBADEL, S. and C. B. BARRETT (2012). Spatial price

adjustment with and without trade. Oxford Bulletin of Economics and Statistics, 74-3: 453-469.

SURFACE TRANSPORTATION BOARD (2016): Carload Waybill Samples. www.stb.gov.

USDA AMS (2016): Market News online database, https://www.marketnews.usda.gov

USDA AMS (2017): Transportation Services Division, https://www.ams.usda.gov/services/transportation-

analysis

USDA ERS (2017): Feed Grains Database, https://www.ers.usda.gov/data-products/feed-grains-database/

USDA NASS (2016): Crop Production 2015 Summary (January, 2016).

USDA NASS (2017): Quick stats database, https://quickstats.nass.usda.gov/

USDA WASDE (2017): World Agricultural Supply and Demand Estimates, WASDE-563 (March 9, 2017)

https://www.usda.gov/oce/commodity/wasde/

Appendix

Table A1. Augmented Dickey-Fuller test for prices in levels and first differences

Variable Determ.

component

Lags Test-stat Δ Variable Determ.

component

Lags Test-stat

Russia (regional)

07/2005 – 12/2012

Central Constant & trend 2 -2.531 Δ Central None 1 -7.396***

North Caucasus Constant & trend 1 -2.287 Δ North Caucasus None 0 -10.14***

Black Earth Constant & trend 1 -2.362 Δ Black Earth None 0 -8.520***

Volga Constant & trend 2 -2.569 Δ Volga None 1 -7.252***

Urals Constant & trend 2 -2.380 Δ Urals None 1 -7.351***

West Siberia Constant & trend 2 -2.546 Δ West Siberia None 1 -7.349***

North Caucasus

07/2011 – 09/2016

Adygeya None 1 -0.085 Δ Adygeya None 0 -9.628***

Krasnodar Constant 1 -2.412 Δ Krasnodar None 0 -8.827***

Rostov Constant & trend 1 -2.674 Δ Rostov None 0 -9.943***

Stavropol Constant 0 -2.022 Δ stavropol None 0 -10.41***

West Siberia

07/2011 – 09/2016

Altai Constant 1 -2.356 Δ Altai None 0 -6.979***

Kemerovo Constant 1 -2.343 Δ Kemerovo None 0 -9.098***

Novosibirsk Constant & trend 0 -1.851 Δ Novosibirsk None 0 -9.688***

Omsk Constant & trend 1 -2.712 Δ Omsk None 0 -8.317***

Tomsk Constant 1 -2.052 Δ Tomsk None 0 -6.737***

Tyumen Constant & trend 0 -2.460 Δ Tyumen None 0 -11.38***

USA (regional)

07/2008 – 06/2011

Tyumen

Arkansas Constant & trend 1 -3.018 Δ Arkansas None 0 -10.22***

California Constant & trend 0 -3.080 Δ California None 0 -11.94***

Colorado Constant & trend 1 -2.548 Δ Colorado None 0 -10.19***

Illinois Constant & trend 1 -2.816 Δ Illinois None 0 -10.14***

Iowa Constant & trend 1 -2.753 Δ Iowa None 0 -10.10***

Kansas Constant & trend 1 -2.607 Δ Kansas None 0 -9.961***

Minnesota Constant & trend 0 -2.940 Δ Minnesota None 0 -10.31***

Missouri Constant & trend 0 -3.144* Δ Missouri None 0 -10.36***

Nebraska Constant & trend 0 -2.849 Δ Nebraska None 0 -10.54***

Oklahoma Constant & trend 0 -2.878 Δ Oklahoma None 0 -10.96***

28

Oregon Constant & trend 0 -3.088 Δ Oregon None 0 -10.32***

S. Dakota Constant & trend 0 -2.822 Δ S. Dakota None 0 -10.71***

Texas Constant & trend 0 -2.948 Δ Texas None 0 -10.25***

Virginia Constant & trend 0 -3.038 Δ Virginia None 0 -10.35***

Washington Constant & trend 0 -3.152* Δ Washington None 0 -10.41***

Wyoming Constant & trend 0 -2.688 Δ Wyoming None 0 -10.50***

Iowa

07/2010 – 06/2012

Cedar Rapids Constant & trend 0 -2.915 Δ Cedar Rapids None 0 -21.64***

Clinton Constant & trend 0 -3.031 Δ Clinton None 0 -21.91***

Davenport Constant & trend 0 -3.212* Δ Davenport None 0 -21.02***

Eddyville Constant & trend 0 -2.944 Δ Eddyville None 0 -22.29***

Emmetsburg Constant & trend 0 -3.026 Δ Emmetsburg None 0 -21.91***

Keokuk Constant & trend 0 -3.266* Δ Keokuk None 0 -21.14***

Muscatine Constant & trend 0 -3.127* Δ Muscatine None 0 -21.48***

W. Burlington Constant & trend 0 -3.268* Δ W. Burlington None 0 -21.32***

North Carolina

07/2010 – 06/2012

Candor Constant & trend 0 -2.844 Δ Candor None 0 -23.36***

Cofield Constant & trend 0 -2.984 Δ Cofield None 0 -22.34***

Creswell Constant & trend 0 -2.842 Δ Creswell None 0 -23.48***

Laurinburg Constant & trend 0 -2.906 Δ Laurinburg None 0 -22.33***

Roaring River Constant & trend 1 -2.916 Δ Roaring River None 0 -27.18***

Rose Hill Constant & trend 0 -2.906 Δ Rose Hill None 0 -22.32***

Statesville Constant & trend 0 -3.123* Δ Statesville None 0 -21.88***

Note: Prices are given in natural logarithm. The asterisks refer to the significance at 1% (***), 5% (**) and 10% (*)

levels. Lag length selection is based on Schwarz Information Criterion. One-sided p-values are from MacKinnon

(1996).

Source: Own calculations.

Table A2. Tests of threshold cointegration:

a) Russia (regional) 2009/10

Price series

Specification Hansen & Seo test

(2002) a

Larsen test

(2012) b

Intercept Lags Sup-Wald

test statistic

5% critical

value P-value

Central – Black Earth yes 1 11.111 18.398 0.061

Central – Volga yes 2 20.817* 21.537 0.512

Central – Urals no 2 20.363*** 18.295 0.140

Central – West Siberia yes 2 19.219* 20.598 0.100

North Caucasus – Central no 2 21.037*** 20.011 0.033

North Caucasus – Black Earth no 1 13.932* 14.233 0.082

North Caucasus – Volga no 2 21.666*** 18.378 0.043

North Caucasus – Urals no 2 24.227*** 18.548 0.008

North Caucasus – West Siberia no 2 20.543** 19.168 0.040

Black Earth – Volga yes 3 24.383* 05.088 0.070

Black Earth – Urals yes 3 25.332*** 24.907 0.010

Black Earth – West Siberia yes 1 15.223* 16.237 0.080

Volga – Urals no 2 17.746* 18.340 0.417

Volga – West Siberia no 1 12.149* 13.192 0.076

Urals – West Siberia no 2 18.002* 18.360 0.507 Note: Sample runs from 7/3/2009 to 6/25/2010 (52 obs.)

b) Russia (regional) 2010/11

Price series

Specification Hansen & Seo test

(2002) a

Larsen test

(2012) b

Intercept Lags Sup-Wald

test statistic

5% critical

value P-value

29

Central – Black Earth no 2 18.477** 18.042 0.032

Central – Volga no 2 18.477*** 17.512 0.027

Central – Urals no 2 20.360** 19.903 0.080

Central – West Siberia yes 1 16.407* 17.643 0.358

North Caucasus – Central yes 1 15.189** 14.963 0.081

North Caucasus – Black Earth yes 1 15.038* 15.524 0.047

North Caucasus – Volga no 3 23.181** 23.167 0.030

North Caucasus – Urals no 2 23.871*** 17.998 0.130

North Caucasus – West Siberia yes 1 14.983 17.988 0.264

Black Earth – Volga no 3 23.722** 13.249 0.446

Black Earth – Urals no 1 11.489 13.571 0.169

Black Earth – West Siberia no 3 25.341*** 23.204 0.040

Volga – Urals no 3 24.684** 23.650 0.203

Volga – West Siberia no 3 24.313** 23.285 0.108

Urals – West Siberia no 1 8.536 13.635 0.132 Note: Sample runs from 7/2/2010 to 6/24/2011 (52 obs.)

d) North Caucasus (Russia)

Price series

Specification Hansen & Seo test

(2002) a

Larsen test

(2012) b

Intercept Lags Sup-Wald

test statistic

5% critical

value P-value

Adygea – Krasnodar no 5 33.365** 33.132 0.017

Adygea – Rostov yes 5 16.635 22.353 0.071

Adygea – Stavropol yes 5 29.552* 31.008 0.032

Krasnodar – Rostov no 5 19.929*** 14.370 0.005

Krasnodar – Stavropol no 5 14.829** 14.603 0.059

Rostov – Stavropol no 5 19.970*** 14.208 0.002 Note: Sample runs from 7/01/2011 to 9/01/2016 (125 obs.). Sample containing Adygea runs from 7/01/2013 to 9/01/2016 (77

obs.).

e) West Siberia (Russia)

Price series

Specification Hansen & Seo test

(2002) a

Larsen test

(2012) b

Intercept Lags Sup-Wald

test statistic

5% critical

value P-value

Kemerovo – Altai yes 2 23.137** 22.828 0.039

Kemerovo – Novosibirsk yes 2 27.360*** 21.975 0.029

Kemerovo – Omsk no 1 20.482*** 14.081 0.009

Kemerovo – Tomsk no 2 19.149* 20.155 0.067

Novosibirsk – Altai no 2 19.790* 20.568 0.307

Novosibirsk – Omsk no 1 19.122*** 14.382 0.009

Novosibirsk – Tyumen no 1 11.444 14.608 0.037

Tomsk – Novosibirsk no 1 19.151*** 15.103 0.013

Tomsk – Altai no 2 21.258** 19.785 0.126

Tomsk – Omsk no 1 19.094*** 14.804 0.004

Tomsk – Tyumen no 1 16.832** 15.465 0.044

Altai – Omsk yes 3 27.514* 27.819 0.021

Altai – Tyumen no 1 15.165* 15.171 0.003

Tyumen – Kemerovo yes 2 22.884** 21.928 0.002

Tyumen – Omsk no 1 20.173*** 15.508 0.001 Note: Sample runs from 7/01/2011 to 9/01/2016 (125 obs.). Sample with Kemerovo runs from 9/01/2012 to 9/01/2016 (97 obs.).

f) Iowa (the USA)

Price series Specification Hansen & Seo test

(2002) a

Larsen test

(2012) b

30

Intercept Lags Sup-Wald

test statistic

5% critical

value P-value

Cedar Rapids – Emmetsburg no 3 24.431 28.050 0.217

Clinton – Cedar Rapids no 2 27.187*** 22.215 0.074

Clinton – Davenport no 2 24.477** 23.376 0.066

Clinton – Emmetsburg no 2 19.137 23.281 0.061

Clinton – Muscatine yes 2 25.395* 26.041 0.209

Davenport – Cedar Rapids no 2 20.054 23.636 0.176

Davenport – S. Emmetsburg no 2 32.241*** 23.040 0.001

Eddyville – Cedar Rapids no 2 25.386** 23.167 0.019

Eddyville – Clinton no 2 21.868* 22.735 0.074

Eddyville – Davenport no 2 35.571*** 23.287 0.000

Eddyville – Emmetsburg no 2 29.665*** 23.347 0.013

Eddyville – Keokuk no 2 30.147*** 23.205 0.007

Eddyville – Muscatine no 4 28.437 33.871 0.190

Keokuk – Cedar Rapids no 4 31.472 33.946 0.250

Keokuk – Clinton no 1 25.649*** 16.836 0.001

Keokuk – Davenport no 2 30.148*** 23.290 0.006

Keokuk – Emmetsburg no 2 20.434 22.508 0.153

Keokuk – Muscatine no 2 26.417*** 22.629 0.026

Muscatine – Cedar Rapids no 2 32.400*** 17.332 0.000

Muscatine – Davenport no 2 21.769* 23.465 0.020

Muscatine – Emmetsburg no 2 30.974*** 22.924 0.016

West Burlington – Cedar Rapids no 4 39.530*** 33.104 0.015

West Burlington – Clinton no 2 20.466 22.638 0.342

West Burlington – Davenport no 4 36.641** 34.202 0.033

West Burlington – Eddyville no 4 39.114*** 33.906 0.026

West Burlington – Emmetsburg no 2 21.803* 22.424 0.290

West Burlington – Keokuk no 4 31.696* 33.709 0.059 Note: Sample runs from 6/25/2010 to 6/01/2012 (506 obs.). The asterisks refer to the significance at 1% (***), 5% (**) and 10%

(*) levels.

g) North Carolina (the USA)

Price series

Specification Hansen & Seo test

(2002) a

Larsen test

(2012) b

Intercept Lags Sup-Wald

test statistic

5% critical

value P-value

Candor – Creswell no 2 23.120** 21.675 0.007

Cofield – Candor no 1 23.179*** 17.185 0.005

Cofield – Creswell no 1 34.182*** 17.031 0.000

Laurinburg – Candor no 3 30.420** 27.055 0.150

Laurinburg – Cofield no 3 31.259** 28.029 0.134

Laurinburg – Creswell no 2 24.169** 22.374 0.064

Laurinburg – Roaring River no 3 20.899 28.304 0.604

Laurinburg – Statesville no 3 30.895** 27.819 0.016

Roaring River – Candor no 3 33.271*** 27.880 0.283

Roaring River – Cofield no 4 31.830* 33.638 0.007

Roaring River – Creswell no 4 30.025 33.727 0.533

Roaring River – Statesville no 3 28.335 28.316 0.033

Statesville – Candor no 3 35.062*** 27.807 0.060

Statesville – Cofield no 2 26.437*** 22.143 0.009

Statesville – Creswell no 2 44.873*** 21.756 0.000 Note: Sample runs from 6/25/2010 to 6/01/2012 (506 obs.). The asterisks refer to the significance at 1% (***), 5% (**) and 10%

(*) levels.a: H0: linear cointegration | H1: threshold cointegration. 1 threshold, trimming parameter is 0.05, number of bootstrapping

is set to 1000, type of bootstrapping is ‘fixed Regression’. b: H0: linear cointegration | H1: threshold cointegration. 2 thresholds, trimming parameter is 0.05, number of bootstrapping is set to

1000, type of bootstrapping is ‘fixed Regression’.

31

Source: Own estimations.

Table A3: Parameters of the long-run price equilibrium regression

a) North Caucasus (Russia)

Dependent variable Independent

variable Distance (km)

Long-run price

transmission elasticities

Intercept

parameter

Adygea Krasnodar 132 0.915*** 0.777

Adygea Rostov 344 0.863*** 1.182

Adygea Stavropol 229 0.763*** 2.159***

Krasnodar Rostov 277 0.867*** 1.165***

Krasnodar Stavropol 297 0.745*** 2.309***

Rostov Stavropol 343 0.799*** 1.863***

b) West Siberia (Russia)

Dependent variable Independent

variable Distance (km)

Long-run price

transmission elasticities

Intercept

parameter

Kemerovo Altai 411 0.526*** 4.925***

Kemerovo Novosibirsk 267 0.649*** 3.219***

Kemerovo Omsk 906 0.549*** 4.134***

Kemerovo Tomsk 218 0.595*** 3.632***

Novosibirsk Altai 226 0.916*** 0.690**

Novosibirsk Omsk 654 0.843*** 1.440***

Novosibirsk Tyumen 1280 0.888*** 1.055***

Tomsk Novosibirsk 268 0.876*** 1.230***

Tomsk Altai 490 0.920*** 0.782***

Tomsk Omsk 911 0.807*** 1.879***

Tomsk Tyumen 1538 0.841*** 1.589***

Altai Omsk 880 0.861*** 1.338***

Altai Tyumen 1504 0.882*** 1.173***

Tyumen Kemerovo 1548 0.879*** 1.016

Tyumen Omsk 624 0.832*** 1.480***

Tyumen Omsk 624 0.832*** 1.480***

c) Iowa (the USA)

Dependent variable Independent

variable Distance (km)

Long-run price

transmission elasticities

Intercept

parameter

Cedar Rapirs Emmetsburg 354 0.918*** 0.483***

Clinton Cedar Rapids 138 0.984*** 0.097***

Clinton Davenport 66 0.979*** 0.139***

Clinton Emmetsburg 489 0.905*** 0.564***

Clinton Muscatine 114 0.984*** 0.100***

Davenport Cedar Rapids 129 0.992*** 0.027

Davenport Emmetsburg 483 0.919*** 0.459***

Eddyville Cedar Rapids 174 0.964*** 0.190***

Eddyville Clinton 290 0.978*** 0.104***

Eddyville Davenport 240 0.963*** 0.210***

Eddyville Emmetsburg 367 0.889*** 0.633***

Eddyville Keokuk 182 0.991*** 0.036

Eddyville Muscatine 166 0.966*** 0.181***

Keokuk Cedar Rapids 188 0.968*** 0.180***

Keokuk Clinton 253 0.982*** 0.096***

Keokuk Davenport 190 0.969*** 0.191***

Keokuk Emmetsburg 542 0.893*** 0.626***

Keokuk Muscatine 140 0.971*** 0.170***

Muscatine Cedar Rapids 105 0.994*** 0.031

Muscatine Davenport 48 0.991*** 0.063**

32

Muscatine Emmetsburg 462 0.916*** 0.490***

West Burlington Cedar Rapids 159 0.963*** 0.203***

West Burlington Clinton 193 0.978*** 0.115***

West Burlington Davenport 126 0.965*** 0.211***

West Burlington Eddyville 151 0.997*** 0.008

West Burlington Emmetsburg 512 0.888*** 0.647***

West Burlington Keokuk 66 0.993*** 0.035**

d) North Carolina (the USA)

Dependent variable Independent

variable Distance (km)

Long-run price

transmission elasticities

Intercept

parameter

Candor Creswell 360 0.864*** 0.846***

Cofield Candor 333 0.994*** -0.009

Cofield Creswell 97 0.872*** 0.760***

Laurinburg Candor 71 0.925*** 0.396***

Laurinburg Cofield 343 0.927*** 0.426***

Laurinburg Creswell 370 0.812*** 1.110***

Laurinburg Roaring River 261 0.968*** 0.159***

Laurinburg Statesville 211 0.906*** 0.536***

Roaring River Candor 192 0.943*** 0.321***

Roaring River Cofield 286 0.935*** 0.400***

Roaring River Creswell 475 0.819*** 1.093***

Roaring River Statesville 65 0.918*** 0.488***

Statesville Candor 157 1.009*** -0.082*

Statesville Cofield 439 1.005*** -0.018

Statesville Creswell 470 0.878*** 0.736***

Note: The asterisks refer to the significance at 1% (***), 5% (**) and 10% (*) levels.

Source: Own estimations.

Table A4. Results of TVECM:

a) North Caucasus, Russia

Price pair Lower regime Middle regime Upper regime Total adjustment

Number of obs.

Dependent – indep. variable 𝝆𝟏 [Pvalue] Lower

Thresh.

𝝆𝟐 [Pvalue] Upper

Thresh.

𝝆𝟑 [Pvalue] Lower Middle Upper Band of

inaction

1 Adygeya - Krasnodar -0.306 0.000 -0.109 0.027 0.475 0.084 -0.078 0.196 0.306 - - 0.192

Krasnodar - Adygeya 0.115 0.330 -0.066 0.441 0.133 0.215 8 47 14

2 Adygeya - Stavropol -0.222 0.002 -0.131 -0.103 0.122 0.107 -0.095 0.137 0.222 - - 0.238

Stavropol - Adygeya -0.130 0.057 0.036 0.570 0.050 0.414 11 53 10

3 Adygeya -Urals -0.237 0.001 -0.136 -0.111 0.043 0.089 -0.111 0.043 0.237 0.111 0.111 0.224

Urals - Adygeya -0.036 0.679 0.131 0.142 0.131 0.142 7 52 15

4 Krasnodar - Rostov -0.513 0.064 -0.140 -0.148 0.018 0.143 0.316 0.748 0.513 0.148 - 0.283

Rostov - Krasnodar -0.689 0.032 -0.086 0.302 0.989 0.321 3 117 2

5 Krasnodar - Stavropol -0.117 0.458 -0.223 -0.151 0.016 0.122 -0.129 0.102 0.486 0.384 0.129 0.345

Stavropol - Krasnodar 0.486 0.007 0.232 0.012 0.059 0.580 1 114 8

6 Rostov - Stavropol -0.164 0.359 -0.283 -0.085 0.289 0.041 -0.130 0.158 0.730 0.219 0.148 0.325

Stavropol - Rostov 0.730 0.000 0.219 0.006 0.148 0.011 1 84 38

b) West Siberia, Russia

Price pair Lower regime Middle regime Upper regime Total adjustment

Number of obs.

Dependent – indep. variable 𝝆𝟏 [Pvalue] Lower

Thresh.

𝝆𝟐 [Pvalue] Upper

Thresh.

𝝆𝟑 [Pvalue] Lower Middle Upper Band of

inaction

1 Kemerovo – Altai -0.204 0.037 -0.176 -0.198 0.038 0.049 -0.172 0.066 0.204 0.198 0.172 0.226

Altai – Kemerovo -0.127 0.225 -0.096 0.353 -0.188 0.062 4 60 30

2 Kemerovo – Novosibirsk -0.483 0.006 -0.085 -0.063 0.576 0.060 -0.063 0.576 0.483 0.331 0.331 0.146

Novosibirsk – Kemerovo 0.151 0.429 0.331 0.014 0.331 0.014 8 73 13

3 Kemerovo – Omsk -0.318 0.026 -0.120 -0.191 0.038 0.147 -0.366 0.015 0.318 0.191 0.366 0.267

Omsk – Kemerovo -0.012 0.940 0.124 0.319 -0.175 0.317 5 86 2

4 Kemerovo – Tomsk -0.786 0.000 -0.099 -0.042 0.578 0.088 -0.491 0.018 0.786 - 0.491 0.187

Tomsk – Kemerovo -0.285 0.218 0.013 0.917 -0.577 0.020 11 78 5

5 Novosibirsk – Altai -0.456 0.000 -0.257 -0.364 0.000 0.157 -0.333 0.000 0.456 0.364 0.333 0.413

Altai – Novosibirsk -0.076 0.297 -0.047 0.459 -0.037 0.518 4 108 10

6 Novosibirsk – Omsk -0.304 0.001 -0.166 -0.149 0.009 0.192 -0.310 0.002 0.304 0.383 0.310 0.358

Omsk – Novosibirsk 0.099 0.410 0.235 0.003 0.117 0.335 7 112 4

7 Novosibirsk – Tyumen 0.126 0.321 -0.364 -0.133 0.010 0.105 -0.042 0.450 0.310 0.133 0.157 0.469

34

Tyumen – Novosibirsk 0.310 0.030 0.085 0.201 0.157 0.027 1 93 29

8 Tomsk – Novosibirsk -0.024 0.719 -0.128 0.026 0.738 0.157 0.024 0.769 0.329 0.243 0.416 0.285

Novosibirsk – Tomsk 0.329 0.000 0.243 0.001 0.416 0.000 11 107 5

9 Tomsk – Altai -0.386 0.001 -0.047 -0.497 0.002 0.014 -0.362 0.000 0.386 0.497 0.362 0.061

Altai – Tomsk 0.015 0.896 -0.084 0.606 0.092 0.374 16 61 45

10 Tomsk – Omsk -0.194 0.025 -0.068 -0.079 0.447 0.105 -0.049 0.547 0.194 0.261 0.407 0.172

Omsk – Tomsk 0.155 0.120 0.261 0.026 0.407 0.000 20 95 8

11 Tomsk – Tyumen 0.017 0.805 -0.176 -0.024 0.558 0.132 -0.002 0.971 0.361 0.214 0.224 0.308

Tyumen – Tomsk 0.361 0.000 0.214 0.001 0.224 0.003 7 94 22

12 Altai – Omsk -0.177 0.044 -0.113 0.216 0.048 0.019 0.104 0.221 0.177 0.441 0.461 0.132

Omsk – Altai 0.165 0.118 0.441 0.001 0.461 0.000 11 51 59

13 Altai – Tyumen -0.072 0.138 -0.225 0.008 0.816 0.171 0.008 0.816 0.241 0.208 0.208 0.396

Tyumen – Altai 0.241 0.001 0.208 0.001 0.208 0.001 6 100 16

14 Tyumen – Kemerovo -0.089 0.368 -0.109 -0.175 0.157 0.101 -0.089 0.368 - - - 0.210

Kemerovo – Tyumen 0.110 0.128 -0.086 0.358 0.110 0.128 19 48 17

15 Tyumen – Omsk -0.322 0.000 -0.114 -0.318 0.000 0.125 -0.322 0.000 0.322 0.318 0.322 0.239

Omsk – Tyumen -0.002 0.972 -0.004 0.951 -0.002 0.972 27 74 12

c) Iowa, the USA

Price pair Lower regime Middle regime Upper regime Total adjustment

Number of obs.

Dependent – indep. variable 𝝆𝟏 [Pvalue] Lower

Thresh.

𝝆𝟐 [Pvalue] Upper

Thresh.

𝝆𝟑 [Pvalue] Lower Middle Upper Band of

inaction

1 Cedar Rapids – Emmetsburg -0.812 0.063 -0.012 -0.807 0.067 0.008 -0.812 0.063 0.812 0.807 0.812 0.020

Emmetsburg – Cedar Rapids -0.554 0.372 -0.549 0.381 -0.554 0.372 121 226 153

2 Clinton – Cedar Rapids -0.959 0.387 -0.024 -0.996 0.053 0.012 -0.887 0.552 - 0.996 - 0.037

Cedar Rapids – Clinton 0.983 0.279 -0.695 0.621 0.982 0.303 3 481 16

3 Clinton – Davenport -0.891 0.083 -0.009 0.818 0.130 0.039 -0.891 0.083 0.891 - 0.891 0.048

Davenport – Clinton -0.204 0.851 0.239 0.803 -0.204 0.851 158 334 8

4 Clinton – Emmetsburg -0.879 0.046 -0.001 0.795 0.191 0.021 -0.921 0.028 0.879 - 0.921 0.021

Emmetsburg – Clinton -0.648 0.317 0.452 0.613 -0.702 0.280 254 190 56

5 Clinton – Muscatine 0.783 0.477 -0.025 0.825 0.293 0.017 0.629 0.618 0.999 - 0.998 0.042

Muscatine – Clinton 0.999 0.010 0.808 0.313 0.998 0.009 9 460 31

6 Davenport – Cedar Rapids 0.418 0.662 -0.032 0.491 0.523 0.028 -0.491 0.523 0.899 - - 0.059

Cedar Rapids – Davenport 0.899 0.075 0.468 0.537 0.468 0.537 26 460 14

7 Davenport – Emmetsburg -0.917 0.059 -0.023 -0.960 0.022 0.010 -0.921 0.054 0.917 0.960 0.921 0.033

Emmetsburg – Davenport -0.319 0.754 0.711 0.346 -0.372 0.706 18 377 105

8 Eddyville – Cedar Rapids 0.460 0.733 -0.045 0.626 0.373 0.028 0.460 0.733 0.986 - 0.986 0.073

35

Cedar Rapids – Eddyville 0.986 0.035 0.541 0.463 0.986 0.035 6 473 21

9 Eddyville – Clinton -0.516 0.666 -0.028 0.506 0.538 0.032 -0.114 0.945 - - 0.988 0.060

Clinton – Eddyville 0.927 0.138 0.642 0.363 0.988 0.027 20 462 18

10 Eddyville – Davenport -0.688 0.238 -0.002 0.750 0.463 0.001 -0.637 0.282 - - - 0.003

Davenport – Eddyville 0.429 0.561 0.387 0.788 0.486 0.473 252 37 211

11 Eddyville – Emmetsburg -0.958 0.016 -0.034 -0.900 0.034 0.022 -0.668 0.165 0.958 0.900 - 0.056

Emmetsburg – Eddyville -0.828 0.163 -0.689 0.267 0.100 0.891 20 400 83

12 Eddyville – Keokuk -0.981 0.026 -0.026 -0.954 0.031 0.014 -0.880 0.143 0.981 0.954 - 0.041

Keokuk – Eddyville -0.299 0.819 0.425 0.668 0.735 0.340 17 414 72

13 Eddyville – Muscatine 0.058 0.965 -0.031 0.329 0.725 0.023 0.116 0.920 - - 0.900 0.054

Muscatine – Eddyville 0.876 0.139 0.729 0.251 0.900 0.075 11 449 40

14 Keokuk – Cedar Rapids -0.603 0.574 -0.033 0.082 0.938 0.040 -0.651 0.542 - - - 0.073

Cedar Rapids – Keokuk 0.810 0.326 0.828 0.128 0.814 0.340 14 485 1

15 Keokuk – Clinton 0.999 0.090 -0.060 0.189 0.845 0.033 0.999 0.090 0.001 - 0.060 0.093

Clinton – Keokuk 0.999 0.010 0.830 0.116 0.999 0.010 1 492 7

16 Keokuk – Davenport -0.928 0.073 -0.026 0.087 0.940 0.016 -0.917 0.090 0.928 - 0.917 0.042

Davenport – Keokuk 0.279 0.815 0.877 0.121 0.333 0.773 13 425 62

17 Keokuk – Emmetsburg -0.553 0.289 -0.011 -0.553 0.289 0.035 -0.553 0.289 - - - 0.046

Emmetsburg – Keokuk 0.259 0.695 0.259 0.695 0.259 0.695 139 343 21

18 Keokuk – Muscatine 0.595 0.415 -0.021 0.589 0.411 0.027 0.592 0.416 0.924 0.917 0.922 0.048

Muscatine – Keokuk 0.924 0.027 0.917 0.028 0.922 0.028 22 455 23

19 Muscatine – Cedar Rapids -0.889 0.260 -0.041 -0.989 0.008 0.012 -0.915 0.207 - 0.989 - 0.053

Cedar Rapids – Muscatine 0.735 0.483 -0.678 0.459 0.557 0.659 2 424 74

20 Muscatine – Davenport -0.907 0.024 -0.008 -0.905 0.025 0.011 -0.905 0.025 0.907 0.905 0.905 0.019

Davenport – Muscatine -0.457 0.548 -0.449 0.559 -0.449 0.559 170 202 128

21 Muscatine – Emmetsburg -0.910 0.021 -0.013 -0.910 0.021 0.012 -0.918 0.009 0.910 0.910 0.918 0.024

Emmetsburg – Muscatine -0.729 0.204 -0.729 0.204 -0.722 0.180 109 277 114

22 W. Burlington – Cedar Rapids 0.999 0.034 -0.066 -0.537 0.470 0.039 0.999 0.034 0.999 - 0.999 0.105

Cedar Rapids – W. Burlington 0.999 0.003 0.360 0.675 0.999 0.003 1 494 5

23 W. Burlington – Clinton 0.999 0.001 -0.063 -0.757 0.225 0.025 0.999 0.001 0.999 - 0.999 0.088

Clinton – W. Burlington 0.999 0.000 -0.046 0.967 0.999 0.000 1 487 12

24 W. Burlington – Davenport -0.731 0.642 -0.033 -0.605 0.422 0.031 0.871 0.400 - - 0.996 0.063

Davenport – W. Burlington 0.678 0.692 0.105 0.926 0.996 0.053 3 486 11

25 W. Burlington – Eddyville -0.079 0.961 -0.034 -0.194 0.876 0.016 0.503 0.671 -0.960 -0.945 -0.918 0.050

Eddyville – W. Burlington -0.960 0.100 -0.945 0.067 -0.918 0.167 2 440 58

26 W. Burlington – Emmetsburg -0.580 0.245 -0.012 -0.201 0.796 0.025 -0.201 0.796 - - - 0.036

Emmetsburg – W. Burlington -0.081 0.912 0.438 0.529 0.438 0.529 139 322 42

27 W. Burlington – Keokuk -0.983 0.132 -0.021 -0.996 0.036 0.014 -0.947 0.256 - 0.996 - 0.035

Keokuk – W. Burlington -0.066 0.977 -0.733 0.571 0.590 0.717 10 459 31

36

d) North Carolina, the USA

Price pair Lower regime Middle regime Upper regime Total adjustment

Number of obs.

Dependent – indep. variable 𝝆𝟏 [Pvalue] Lower

Thresh.

𝝆𝟐 [Pvalue] Upper

Thresh.

𝝆𝟑 [Pvalue] Lower Middle Upper Band of

inaction

1 Candor – Creswell -0.804 0.018 -0.012 -0.468 0.338 0.029 -0.475 0.300 0.804 - - 0.040

Creswell – Candor -0.588 0.241 0.151 0.822 0.187 0.765 236 173 91

2 Cofield – Candor -0.761 0.097 -0.025 -0.773 0.116 0.019 -0.773 0.116 0.761 - - 0.044

Candor – Cofield -0.449 0.457 -0.494 0.439 -0.494 0.439 52 352 96

3 Cofield – Creswell -0.942 0.001 -0.043 -0.926 0.002 0.024 -0.761 0.067 0. 942 0. 926 0.761 0.067

Creswell – Cofield -0.798 0.070 -0.761 0.098 -0.257 0.719 4 409 87

4 Laurinburg – Candor -0.657 0.123 -0.032 -0.653 0.126 0.015 -0.713 0.061 - - 0.713 0.047

Candor – Laurinburg -0.454 0.361 -0.441 0.379 -0.547 0.216 47 360 93

5 Laurinburg – Cofield -0.831 0.078 -0.049 -0.774 0.119 0.003 -0.820 0.070 0.831 - 0.820 0.051

Cofield – Laurinburg -0.409 0.590 -0.443 0.531 -0.289 0.710 10 276 214

6 Laurinburg – Creswell -0.729 0.035 -0.034 -0.945 0.002 0.010 -0.729 0.035 0. 729 0.945 0.729 0.044

Creswell – Laurinburg -0.458 0.367 -0.886 0.026 -0.458 0.367 34 341 125

7 Laurinburg – Roaring River -0.622 0.227 -0.050 -0.622 0.227 0.037 -0.764 0.586 - - 0.999 0.088

Roaring River – Laurinburg -0.125 0.876 -0.125 0.876 0.999 0.034 2 495 3

8 Laurinburg – Statesville 0.178 0.843 -0.058 -0.730 0.166 0.016 0.178 0.843 0.910 - 0.910 0.074

Statesville – Laurinburg 0.910 0.025 0.049 0.956 0.910 0.025 10 428 62

9 Roaring River – Candor -1.000 0.000 -0.029 -0.871 0.106 0.024 -0.720 0.273 1.000 - - 0.053

Candor – Roaring River -0.965 0.099 -0.780 0.184 -0.277 0.753 11 443 46

10 Roaring River – Cofield -0.991 0.008 -0.144 -0.686 0.210 0.034 -0.686 0.210 0.991 - - 0.178

Cofield – Roaring River -0.103 0.942 -0.070 0.930 -0.070 0.930 3 474 23

11 Roaring River – Creswell -0.997 0.000 -0.125 -0.756 0.027 0.035 -0.756 0.027 0.997 0.756 0.756 0.160

Creswell – Roaring River -0.050 0.965 -0.618 0.132 -0.618 0.132 3 421 76

12 Roaring River – Statesville -0.991 0.026 -0.129 -0.477 0.531 0.028 -0.494 0.543 1.972 0.865 0.900 0.157

Statesville – Roaring River 0.981 0.045 0.865 0.043 0.900 0.036 3 478 19

13 Statesville – Candor 0.832 0.052 -0.051 -0.252 0.690 0.016 0.832 0.052 0.961 - 0.961 0.068

Candor – Statesville 0.961 0.001 0.449 0.429 0.961 0.001 8 398 94

14 Statesville – Cofield 0.204 0.747 -0.034 -0.151 0.828 0.027 -0.214 0.737 0.741 - - 0.061

Cofield – Statesville 0.741 0.062 0.591 0.241 0.539 0.281 28 420 52

15 Statesville – Creswell -0.879 0.005 -0.046 -0.914 0.001 0.004 -0.795 0.003 0.879 0.914 0.795 0.050

Creswell – Statesville 0.617 0.211 -0.710 0.102 -0.544 0.168 28 274 198

Note: Total adjustment in one regime is calculated as the sum of the absolute value of the respective regime-specific speed of adjustment parameters of the TVECM. The band of

inaction is given as the difference between the absolute value of the upper and lower threshold.Speed of adjustment coefficients which have opposite sign than threoretically

exected and are significant at least at 0.10 level are given in italic. Speed of adjustment parameters for Iowa and North Carolina in tabels c) and d) are adjusted from daily to by-

weekly frequencies in order to make coefficients comparable with the estimates for North Caucasus and West Siberia in tables a) and b). Source: Own estimations.

Table A5: Parameters of the long-run price equilibrium regression, the USA 2008/11

Dependent variable Independent variable distance Slope Intercept P-value

Arkansas Illinois 595 0.967 0.177 <0.001

Arkansas Iowa 475 0.974 0.161 <0.001

Arkansas Kansas 993 0.941 0.325 <0.001

Arkansas Minnesota 531 0.953 0.279 <0.001

Arkansas Missouri 393 0.929 0.406 <0.001

Arkansas Nebraska 581 0.979 0.109 0.094

Arkansas South Dakota 1144 0.894 0.593 <0.001

California Illinois 3288 0.764 1.968 <0.001

California Iowa 3084 0.776 1.93 <0.001

California Kansas 2356 0.758 2.019 <0.001

California Minnesota 3224 0.761 2.01 <0.001

California Missouri 2945 0.725 2.189 <0.001

California Nebraska 2675 0.784 1.863 <0.001

California South Dakota 2548 0.715 2.258 <0.001

Colorado Illinois 1720 0.982 0.045 0.369

Colorado Iowa 1273 0.997 -0.003 0.929

Colorado Kansas 494 0.979 0.088 <0.001

Colorado Minnesota 1482 0.978 0.103 <0.001

Colorado Missouri 974 0.929 0.346 <0.001

Colorado Nebraska 866 1.007 -0.082 0.032

Colorado South Dakota 901 0.919 0.418 <0.001

Oklahoma Illinois 1315 1.08 -0.437 <0.001

Oklahoma Iowa 1289 1.093 -0.473 <0.001

Oklahoma Kansas 220 1.078 -0.393 <0.001

Oklahoma Minnesota 1498 1.073 -0.361 <0.001

Oklahoma Missouri 789 1.02 -0.101 0.244

Oklahoma Nebraska 874 1.104 -0.558 <0.001

Oklahoma South Dakota 1073 1.009 -0.016 0.787

Oregon Illinois 3642 0.777 1.656 <0.001

Oregon Iowa 2836 0.788 1.619 <0.001

Oregon Kansas 2472 0.77 1.708 <0.001

Oregon Minnesota 2926 0.774 1.698 <0.001

Oregon Missouri 2895 0.735 1.889 <0.001

Oregon Nebraska 2660 0.796 1.554 <0.001

Oregon South Dakota 2245 0.728 1.945 <0.001

Texas Illinois 1226 0.951 0.26 <0.001

Texas Iowa 1487 0.963 0.223 <0.001

Texas Kansas 380 0.948 0.3 <0.001

Texas Minnesota 1695 0.945 0.322 <0.001

Texas Missouri 985 0.899 0.553 <0.001

Texas Nebraska 1032 0.972 0.148 <0.001

Texas South Dakota 1262 0.89 0.621 <0.001

Virginia Illinois 1349 0.922 0.49 <0.001

Virginia Iowa 1897 0.929 0.478 <0.001

Virginia Kansas 2356 0.909 0.577 <0.001

Virginia Minnesota 1833 0.908 0.588 <0.001

Virginia Missouri 1754 0.879 0.739 <0.001

Virginia Nebraska 2037 0.934 0.421 <0.001

Virginia South Dakota 2565 0.852 0.887 <0.001

Washington Illinois 3375 0.788 1.574 <0.001

Washington Iowa 2393 0.800 1.538 <0.001

Washington Kansas 2351 0.787 1.602 <0.001

Washington Minnesota 2482 0.785 1.618 <0.001

38

Washington Missouri 2628 0.746 1.811 <0.001

Washington Nebraska 2342 0.808 1.473 <0.001

Washington South Dakota 1801 0.739 1.870 <0.001

Wyoming Illinois 1782 0.974 0.088 0.079

Wyoming Iowa 1221 0.989 0.041 0.285

Wyoming Kansas 721 0.97 0.136 <0.001

Wyoming Minnesota 1310 0.97 0.147 <0.001

Wyoming Missouri 1033 0.921 0.387 <0.001

Wyoming Nebraska 800 0.999 -0.037 0.352

Wyoming South Dakota 653 0.912 0.458 <0.001

Note: All slope parameters are significant at a level lower than 1%. Weekly price series include 156 observations

recorded between 2008 and 2011 marketing years. Estimation of long-run price transmission coefficients was

preceeded by identification of linear or threshold cointegration (Johansen, 1988; Hansen and Seo 2006, Larsen,

2012). Tests confirmed existence of cointegration between regional market pairs in the USA. Results are available

from authors upon request.

Source: Own estimations.